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IMPROVING THE FREEZE-THAW DURABILITY OF CONCRETE MASONRY PRODUCTS
February 2007
Table of contents
Task 1..................................................................................................... Literature Survey Task 2..............................................................................................................Test Results Task 3..............................................................................................Frost Durability Index Task 4A....Laboratory Validation of Durability and Assessment of Frost Durability of 13 Existing Masonry Units – Salt Water Task 4B ....Laboratory Validation of Durability and Assessment of Frost Durability of 13 Existing Masonry Units – Tap Water
LLIITTEERRAATTUURREE SSUURRVVEEYY
Project SEM00110
Presented to :
Mr.Robert D. Thomas National Concrete Masonry Association
2302 Horse Pen Road Herndon, VA 20171-3499
August 2001
1
TABLE OF CONTENTS 1 INTRODUCTION..............................................................................................................................................2
2 MATERIALS CHARACTERISTICS AND PRODUCTION ........................................................................3
2.1 MATERIALS SELECTION AND MIXTURE DESIGN..................................................................................................4 2.2 PRODUCTION .......................................................................................................................................................6 2.3 ORDINARY CONCRETE VS. DRY CONCRETE.........................................................................................................7
3 GENERAL CONSIDERATIONS PERTAINING TO THE FROST DURABILITY OF CONCRETE....9
3.1 FUNDAMENTALS OF FROST ACTION.....................................................................................................................9 3.1.1 Internal Microcracking.......................................................................................................................................9 3.1.2 Parameters Influencing Frost Resistance of Ordinary Concrete Mixtures.......................................................13 3.1.3 Deicer salt scaling ............................................................................................................................................15 3.2 LABORATORY TEST ...........................................................................................................................................17
4 THE FROST DURABILITY OF DRY CONCRETE MIXTURES.............................................................20
4.1 INTRODUCTION ..................................................................................................................................................20 4.2 AIR ENTRAINMENT IN DRY CONCRETE MIXTURES ............................................................................................22 4.3 THE FROST DURABILITY OF DRY CONCRETES ...................................................................................................27 4.3.1 The Ability of Dry Concretes to Resist Internal Microcracking .......................................................................28 4.3.2 The Deicer Salt Scaling Resistance of Dry Concrete Elements ........................................................................34
5 CONCLUDING REMARKS...........................................................................................................................37
6 RECOMMENDATIONS.................................................................................................................................39
7 REFERENCES.................................................................................................................................................41
2
1 Introduction
Precast masonry units, made of (no-slump) dry concrete, have been produced and sold
commercially since 1900. Over the years, dry concrete elements have been gradually considered
as an attractive alternative to conventional construction materials due to their economical and
relatively easier way to produce. Applications such as concrete pavers, segmental retaining
walls, roof ballast block and erosion concrete walls have since made their way on the North
American market.
Dry concrete mixtures, like other types of concrete, have the ability to absorb and retain
moisture. This characteristic has an important consequence on their durability since unprotected
concrete elements in contact with water are usually susceptible to frost damage. It has been
clearly established that saturated concrete exposed to repeated freezing and thawing cycles can
be affected by two types of deterioration: internal microcracking and deicer salt scaling (also
known as surface scaling) [1]. It has also been shown that, in practice, each phenomenon can
occur independently of the other.
Over the past decades, numerous reports have clearly demonstrated the beneficial influence of air
entrainment on the frost durability of concrete [1]. The entrainment of a small volume of
spherical air bubbles (normally from 5 to 7% of the total volume of the mixture) is sufficient to
fully protect ordinary concrete against internal microcracking and to markedly increase its
resistance to deicer salt scaling. As will be discussed in the following sections, the necessity and
the possibility of entraining air bubbles in precast dry concrete elements are still topics of debate.
The lack of agreement on these two questions originates, at least partly, from the fact that dry
concrete mixtures have a peculiar air void system. The numerous irregularly shaped compaction
voids that can be observed during the microscopical examinations tend to complicate greatly the
characterization of the air void network. The distinction between the compaction voids and the
3
spherical air bubbles is often time consuming and relies entirely on the operator's experience and
subjectivity. Furthermore, recent experience indicates that air entrainment in these high viscosity
mixtures can be difficult to achieve in practice.
For civil engineers and concrete technologists, the question of air entrainment is quite important
since precast dry concrete elements are frequently exposed to freezing and thawing cycles in
(partially) saturated conditions, and their frost durability is obviously of concern. Over the past
decade, a certain number of concrete masonry products (particularly segmental retaining wall
units) exposed to freezing environments have been found to suffer from premature degradation.
In many instances, the early deterioration was associated with the presence of deicing chemicals
and fertilizers. The significant number of frost related degradation cases has emphasized the need
to further study the mechanisms of frost action and develop reliable test procedures to assess the
frost durability of concrete masonry units.
A critical analysis of the most recent literature on the frost durability of concrete masonry units is
presented in the following sections. After a brief description of the main characteristics and
production of dry concrete mixtures, the basic mechanisms of frost degradation are discussed.
The last section is devoted to the frost durability of dry concrete elements in general and
masonry units more particularly.
2 Materials Characteristics And Production
Nowadays, the dry concrete technology is used to produce a broad range of concrete products
designed for various applications. Although there are slight differences in their constituent
proportions and in the way they are cast, these products all share important common features.
4
Generally speaking, dry concrete products can be defined as concretes with an initial consistency
significantly higher than that of ordinary (initially fluid) mixtures. The dry concrete mixture must
be stiff enough to facilitate an effective consolidation by rollers (as for roller-compacted
concrete) or by a casting machine (as for most precast elements). It must also be wet enough to
allow an adequate distribution of the paste throughout the concrete mass during the mixing and
vibration operations. In most cases, this high consistency is achieved by reducing the amount of
water added to the batch or by lowering the paste fraction of the concrete mixture. Since, as in
granular soils, the final density, and subsequently the overall performance of the product, is
directly affected by the consistency of the mixture, the amount of water and the total aggregate
grading generally have to be controlled precisely. In the following paragraphs, the various
aspects of the production of concrete masonry units (such as materials selection, mixing and
casting operations) will be discussed.
2.1 Materials Selection And Mixture Design
Contrary to ordinary concrete, a good dry mixture must meet several different types of
requirements. As previously mentioned, the fresh mixture must have the right consistency to
facilitate effective consolidation by the casting machine. However, the workability of the mixture
must be also adjusted to allow easy demolding after the compaction operations. To ensure a
smooth production flow with a minimum percentage of rejects, elements must reach a sufficient
strength after a short curing period. Failure to do so will result in a high percentage of broken
elements during handling operations. These requirements can only be met by a proper selection
of the materials used in the production of the mixture. Above all, each constituent should be in
consistent supply. Even small variations in the characteristics of one of the materials can affect
the final quality of the paving block and result in the rejection of a complete production.
The choice of appropriate cement is of importance. In North America, most producers use
ordinary Portland cement (ASTM Type 1). Typically, the minimum cement content tends to vary
5
quite significantly from one type of dry concrete mixtures to another. For instance, the amount of
cement added to masonry units is usually in the range of 200 kg/m3 (approximately 340 lbs/cu
yd) whereas the minimum cement content for roller-compacted concrete (RCC) mixtures (for
pavement applications) are more in the 250 to 300 kg/m3 range (i.e. 420 to 500 lbs/cu yd). In
certain cases, cement contents for the production of concrete paving blocks can even reach 450
kg/m3 (i.e. 760 lbs/cu yd). These amounts are generally sufficient to produce elements with the
required mechanical strength and an adequate appearance. Even if high cement contents tend to
improve overall properties, they significantly raise production costs. Furthermore, a cement
content that is too high will result in a more “plastic” mixture that will stick to the mould and to
the punching plate during consolidation operations.
Supplementary cementing materials, such as silica fume and particularly fly ash, can be added to
masonry concrete products to improve their properties. An investigation published a few years
ago by Marchand et al. [2] indicates that the use of both types of materials tends to increase the
degree of homogeneity of the paste in dry concrete mixtures. In another study, silica fume was
also found to successfully enhance the quality of dry concrete products exposed to severe
weathering conditions [3]. The superior performance the silica fume mixtures was attributed to
reduction of absorption, increased strength, and greater resistance to freezing and thawing.
Added to relatively rich mixtures, silica fume also tends to improve productivity because high
quality product can be formed with lesser compaction times. However, silica fume does impart a
darker gray color to the finish product that can be a problem for retaining segmented wall.
Aggregates used in dry concrete mixtures must be frost resistant and should have adequate
mechanical properties. In North America, producers usually rely on aggregates from natural
sources only since they are generally inexpensive and in good supply. As the costs for natural
aggregates has risen significantly in certain European countries, numerous block producers have
resorted to more economical artificial aggregates such as melting chamber granules [4]. These
are used in conjunction with natural aggregates.
6
Naturally, the overall grading curve and the coarse aggregate maximum size should be suitably
adapted to a given production method and to the dimensions of the paving block. One U.S.
manufacturer (Besser inc.) has proposed a trial grading curve as a starting point for proportioning
an optimum mixture.
Dry concrete producers now increasingly use chemical admixtures. The addition of a water-
reducing agent usually results in an improved homogeneity and an increased density of the
element [5]. It often allows a significant reduction of the water content which helps to achieve
the required compressive strength. Superplasticizers, even at low dosages, have to be avoided
because of demolding problems.
Air-entraining admixtures are sometimes used in the production of dry concrete products.
Usually, the dosages required to entrain a significant number of air bubbles in these “high-
viscosity” mixtures are often much higher than those usually added to ordinary concrete
mixtures. The question of air entrainment and its effects on the frost durability of concrete will
be further discussed in section 4.2.
2.2 Production
Nowadays, most concrete masonry units are made on automated stationary machinery. The
materials are usually prepared in a counter-current pan mixer. The capacity of the mixer is a
critical parameter that often limits the quality of the production. As underlined by Bilgeri [4],
many mixers have an insufficient volume for the production capacity of the factory. This
inevitably results in mixing periods that are too short. At the end of the mixing sequence, the
mixture is poured in the machine drawer, which is placed right above the casting molds.
7
The concrete is compacted to ensure an adequate consolidation of the element. The compaction
area is, in most block making factories, restricted to about 1 m2 (i.e. approximately 11 sq. ft).
Precast dry concrete elements can be made in many different shapes. For instance, a survey of
the international paving block production has indicated that as many as 250 different shapes have
been patented [6].
Two production methods are found in the industry: production on boards and multi-layer
production. In the first method, each layer of paving blocks is placed on a wooden board after the
consolidation operations. Each board in then stored in the curing room for approximately 24
hours. At the end of the curing period, the elements are placed on shipping pallets.
Producers usually rely on two different types of curing. At the end of the casting operations,
most producers place their elements in a closed chamber where the relative humidity is kept over
90%. In order to avoid any premature drying of the element, drafts should be eliminated in the
curing chamber. Steam curing is sometimes used. This reduces the ultimate performance of the
mixture but increases the early strength. Earlier handling is thus possible. In most cases, the
curing period is limited to 24 hours.
2.3 Ordinary Concrete vs. Dry Concrete
The low paste content of dry mixtures has two main consequences on the internal structure of the
hardened concrete [2, 7]. The first one is that the paste is generally less homogeneous because it
is very difficult to properly disperse the cement grains in water. To obtain a good homogeneity,
the mixing energy has to be very high and a longer mixing time is needed. It has also been
observed that the use of supplementary cementitious materials can enhance the homogeneity of
the paste in dry concretes.
8
The second is the formation of irregularly shaped compaction voids due to the incomplete
consolidation of the mixture (Figure 1). The amount and characteristics of these voids depends
on a number of parameters, but the most important probably is the packing density of the
aggregates.
compaction voids
aggregate cement paste Figure 1 — Schematic representation of the structure of dry concrete
It is believed that the presence of compaction voids can help to increasing the frost durability of
dry concrete [2, 7]. If the compaction voids are not connected, they can be as helpful as a small
air voids. The subject will be discussed in more details in a subsequent section.
9
3 General Considerations Pertaining to the Frost Durability of Concrete
3.1 Fundamentals of Frost Action
Freezing and thawing cycles can cause two types of deterioration in any types of concrete:
internal microcracking and surface scaling. Internal microcracking is rarely observed in properly
air-entrained concrete. However, unprotected concrete with a poor air entrained network can be
completely destroyed by frost action. Surface scaling, which occurs generally when deicer salts
are present, is more common even in well air-entrained concrete.
3.1.1 Internal Microcracking
All cementitious materials (dry concrete, ordinary concrete, self leveling concrete…) have
something in common. They contain gel pores, capillary pores and air voids. Figure 2 shows the
range of pore sizes. When the concrete is in moist environment, the gel pores are fully saturated
and the capillary pores system is almost fully saturated. If the concrete contains air voids, those
voids are generally not filled with water. When subzero temperatures are reached, the water in
the capillary pore system freezes. In a concrete with a cement-ratio of 0.4 to 0.5, the capillary
pore can represent 20% of the paste volume. This clearly indicates that there could be a great
amount of “freezable” water in concrete.
10
Figure 2 – Range of pore sizes for the three basic categories of pores in concrete.
Different mechanisms have been suggested to explain the deterioration of concrete by frost
action. The most well known is Powers’ hydraulic pressure theory [8]. According to the author,
the material has to accommodate the 9% volume expansion of water upon freezing. Degradation
is induced by internal stresses generated by the movement of unfrozen (liquid) water expelled
from the capillary pores where ice has formed (see Figure 3).
Over the past decades, many other theories of frost action have been suggested. Probably, the
best known is the so-called osmotic pressure theory developed by Powers and Helmuth [9,10] to
explain the influence of deicer salts. Litvan [11-13] is another investigator that has used
thermodynamics to explain the movement of water to the air voids and the damage due to
freezing and thawing cycles. Critical reviews of these various theories can be found elsewhere
[14, 16].
11
Air void
r
rb
²r
Flow of water
Hydrated cement paste Figure 3 – Air void surrounded by its sphere of influence of hardened cement paste [8]
Irrespective of the mechanism, all theories indicate that closely spaced air voids can relieve the
pressure due to the flow of water. It can also reduce the pressure associated to the formation of
ice in pores, since ice formed in air voids does not generate any internal pressures.
Since internal damage is due to ice formation, as well as to tensile stresses caused by water
movements, the porosity and permeability of the paste are very important parameters as regards
frost resistance. In fact, for a given binder, it is possible to determine a critical water/binder ratio
below which the amount of freezable water is too low to create any significant damage (see
Figure 4) [16].
12
1000
800
0.20 0.30 0.40 0.50 0.60
600
400
200
0
Water-cement ratio
Crit
ical
air-
void
spa
cing
fact
or (µ
m)
Figure 4 — Relationship between critical air-void spacing factor
and water/cement ratio [16]
Of course, the exposure conditions to which the concrete is subjected, such as the degree of
saturation at the time of freezing, the ambient relative humidity, the availability of water during
the freezing and thawing cycles, the freezing rate, the minimum temperature, and the length of
period at the minimum temperature, all have an influence on the durability of concrete. The
importance of the degree of saturation is such that Fagerlund has developed the critical degree of
saturation concept [17, 18]. The latter states that any concrete mixture will become frost
susceptible if the degree of saturation reaches a certain critical value, because long term exposure
to water can eventually cause certain air voids to become saturated [19].
13
3.1.2 Parameters Influencing Frost Resistance of Ordinary Concrete Mixtures
The parameters which can have an influence on the resistance of concrete to freezing and
thawing cycles can be classified into three main categories: the characteristics of the materials
(aggregate, cement and admixtures), the composition of the mixtures (mostly the water-cement
or water/cement ratio) and the exposure conditions (the degree of saturation, the characteristics
of the cycle and those of the drying period before the tests).
In standard good quality concretes with water/cement ratios in the 0,4 to 0,5 range, the type of
cement generally does not have a very significant influence on frost resistance [20], unless
perhaps the cement contains a significant amount of inert filler [21]. Portland cements with
slower reaction rates and cements containing a significant amount of granulated blast furnace
slag (or fly ash) of course require longer curing periods, in order to reduce to acceptable levels
the amount of freezable water. The only really significant exception to this rule is blended silica
fume cement.
The use of silica fume tends to reduce the freezing and thawing cycle durability of ordinary
concretes tested in the laboratory. Although it has been shown that the deterioration of non-air-
entrained concretes due to freezing and thawing cycles is reduced when the paste contains silica
fume [22], it has also been demonstrated that the critical spacing factor is much lower for silica
fume concrete than for normal concrete when the water/binder ratio is equal to 0,5. This is
probably due to the very low permeability of pastes containing silica fume, which makes the
movement of water to the air voids more difficult [23, 24].
With the exception of air-entraining agents, most standard admixtures such as water-reducers,
accelerators or retarding agents have little influence on the frost resistance of concrete. This is
mostly because the spacing factor of the air voids is the dominant parameter as regards frost
resistance. Contrary to what many engineers tend to believe, even the use of superplasticizers
14
does not seem to have a significant effect on the critical spacing factor of normal 0,5
water/cement ratio concrete mixtures [25]. Superplasticizers, of course, can have a large
influence on the parameters of the air void system, and this in turn can affect significantly the
durability of concrete.
All Portland cements are not alike, and the upper limit of the water/cement ratio for good frost
resistance without air entrainment of course varies with the characteristics of the cement that is
used. For instance, it was found that it was not possible to determine the critical spacing factor of
a 0,30 water/cement ratio concrete made with a high early strength Portland cement (ASTM
Type III) and 6% silica fume, even when the tests were carried out after only 24 hours of curing,
because this critical spacing factor was higher than that obtained with the entrapped air voids
only [26]. On the other hand, it was shown that the critical value of the spacing factor for similar
concretes made with an ordinary Portland cement at a water/cement ratio of 0,25 was 750 µm
[27].
Most laboratory freezing and thawing cycle tests are carried out on concrete elements that can be
considered fully saturated. Obviously, the frost resistance of concrete increases very rapidly as
the degree of saturation decreases [28]. The rate of freezing, as most theories point out, can have
a great influence on the action of frost on concrete. This has been verified experimentally. The
length of the freezing period is another significant parameter, but its influence has never been
systematically investigated. It seems clear, however, that long freezing periods promote ice
accretion and can increase the damage due to freezing and thawing cycles.
15
3.1.3 Deicer salt scaling
Scaling is the progressive deterioration of the surface of concrete when it is subjected to freezing
and thawing cycles in the presence of deicer salts. This phenomenon is also possible in the
presence of pure water (i.e. without any salts in solution), but this is not common.
There is no generally accepted theory in the technical documents to explain the influence of
deicer salts, but some aspects of the problem are well known. Evaporation of water is more
difficult in the presence of salts, and cementitious materials subjected to freezing in the presence
of deicer salts thus generally have a higher degree of saturation. Furthermore, most chemicals
have a detrimental effect on the frost durability of concrete. With the presence of salt in the
water, the problem is mostly physical in nature and not directly induced by chemical effects. Air-
entrainment, as previously mentioned, greatly enhances the scaling resistance. A value of 200
µm for the spacing factor (this term will be defined in the following section) is generally
considered necessary to protect normal concrete against scaling due to freezing in the presence of
deicing salts [14, 15].
Verbeck and Klieger [29, 30] have shown that the deterioration that occurs varies with the
concentration of the salt solution. There apparently exists, for each type of deicer salt, a
pessimum concentration at which the deterioration is maximum. For calcium chloride, the value
is around 3% (Figure 5). According to Verbeck and Klieger, unfrozen water is attracted to the
surface where the salt concentration is the highest. The surface cannot accommodate the
incoming water, and water cannot escape to the external surface because ice has formed on the
surface and in the upper layer. This causes pressures that will generally tend to lift the upper
layer, which will results in scaling. This explanation is still disputed in the technical literature.
16
Since scaling is a surface problem, the quality of the surface microstructure (i.e. of the first few
millimeters) is extremely important (Figure 6). The quality of the surface microstructure is
influenced by the method of placement and the curing technique, by bleeding, plastic shrinkage,
as well as long term drying [31]. The water/cement ratio and the use of mineral additives are also
extremely important parameters in this respect [32]. In dry concrete, bleeding and plastic
shrinkage are however negligible.
0 4 8 12 16Concentration of the saline solution (% mass)
5
4
3
2
1
0
Sca
ling
Scale: 0 = no scaling, 5 = very severe scaling
A/E 50 cycles
A/E 200 cycles
Non A/E 50 cycles
Sodium Chloride
Figure 5 — Influence of the concentration of the sodium chloride solution on the degree
of surface scaling due to freezing and thawing cycles [29, 30]
17
Number of freezing and thawing cycles
Mas
s of
resi
dues
Very poor s
calin
g resis
tance
Loss of weak surface layer
Very good scaling resistance
Figure 6 — Typical scaling curves
3.2 Laboratory Test
Although the exposure conditions to which concrete can be subjected are extremely variable and
thus difficult to reproduce in the laboratory, standardized tests are extremely useful to evaluate
the different aspects of the frost resistance of concrete, and also to better understand its behavior
when exposed to freezing and thawing cycles. This section very briefly describes the most
common tests.
The most commonly used test in North America is ASTM Standard C666 [33]. Concrete prisms
are subjected to rapid freezing and thawing cycles (5 to 8 cycles per day up to a total 300). The
freezing-thawing cabinet is shown in Figure 7. Two procedures exist:
18
• Procedure A - freezing and thawing in water
• Procedure B - freezing in air and thawing in water
The latter is generally considered as less severe. The temperature during the test ranges between
5° C and -18° C (40° F and 0° F). The internal deterioration is assessed either by measuring the
residual length change or the evolution of the dynamic modulus of elasticity. Both methods
indicate quite clearly the extent of internal cracking. Similar freezing and thawing tests are in use
in various parts of the world. In Europe, the critical degree of saturation method, which relies on
the measurement of the deterioration due to a small number of freezing and thawing cycles under
different controlled degrees of saturation, is sometimes used.
Figure 7 – Freeze-thaw cabinet for the ASTM C 666 procedure
19
In North America, the deicer salt scaling resistance is generally determined using the ASTM
C672 procedure [34]. Specimens are subjected to daily freezing and thawing cycles (between
room temperature and -18°C) with a salt solution on the exposed surface. The deterioration of
the surface is determined visually. Other standard tests have been developed in North America
and elsewhere to determine the salt scaling resistance. Some are directly applicable to dry
concrete products (such as the Canadian Standards Association test for paving blocks, for
instance (CAN3-A231.2-M85)) [35]. Most of these tests have more or less similar procedures,
i.e. in most cases the concrete samples are exposed to daily freezing and thawing cycle tests in
the presence of a salt solution. The preconditioning of specimens and other conditions can vary
somewhat.
The ASTM standard C1262 [36] is used to evaluate the frost durability of precast concrete
masonry units and related concrete units. After a 24 hours period of saturation in 10 mm water,
the specimens are subjected to freezing and thawing cycles. For the freezing stage, the specimens
are introduced in a chamber at –17 ± 5 °C for a period not less than 4 hours and not more than 5
hours. The thawing stage must take place in a chamber at 24 ± 5 °C for a period of not less than
2.5 hours and not more than 96 hours. After a certain number of cycles (usually 10, 25 and 50
cycles) the scaled-off particles are collected and dried. At the end of the testing period, the
specimens are dried and the final dry mass is taken. The test can be carried in a salt solution or
pure water. For the samples tested with the salt solution, the maximum admissible deterioration
after 40 cycles corresponds to a mass loss of 1% after 40 cycles. In pure water, the limit is also
1% but samples have to be subjected to a minimum number of 100 cycles [36].
ASTM standard C457 [37] is commonly used to determine the characteristics of the air void
system in hardened concrete. Polished samples are examined with an optical microscope (Figure
20
8), and the measurements allow the determination of the air void content, the specific surface of
the air voids and the air-void spacing factor.
Figure 8 – Optical microscope and test set-up for ASTM C457
4 The Frost Durability of Dry Concrete Mixtures
4.1 Introduction
Despite the various advantages offered by dry concrete products, the growth of their use in cold
climate regions has often been impeded by concerns, expressed by several potential users, on
their ability to resist frost attack. The fact that dry concrete elements generally have an internal
structure significantly different from that of ordinary concrete mixtures (see section 2.4) does not
allow the prediction of the service life of these new materials on the basis of the common
21
theories of frost action. The theories described in the previous section are only applicable to
conventional cast-in-place concretes, which have a sufficient initial fluidity to be considered
relatively homogeneous. Unless special problems occur, the coarse as well as the fine aggregate
particles are completely surrounded by the cement paste in which the clinker grains (and thus the
capillary cavities containing the freezable water) are relatively well dispersed. Air voids are
always present, even when the concrete is not air-entrained, but they are almost all spherical in
shape and fairly well distributed in the paste. In such concretes, the average size of the pores,
which can contain freezable water, is at least one hundred times smaller than that of the air voids.
Under normal service conditions these air voids never become saturated and can always act as
escape areas where water can freeze without creating any internal stresses. It can therefore be
considered that each void protects a certain volume of cement paste with a certain freezable
water content. The distance between these air voids determines the volume of paste that each
void must protect and thus the largest distance that water must travel to reach an air void.
As previously underlined, dry concrete mixtures must be stiff enough to allow an effective
consolidation under pressure and vibration. These concretes are characterized by a paste content,
which under normal conditions would be insufficient to allow full compaction. The paste content
of dry mixtures often represents only about 20% of the total volume, as compared to 30% for
normal mixtures. As previously emphasized, the high consistency that is required for dry
concretes has several consequences on their pore structure that contribute to distinguish them
from conventional concretes. When polished sections of such concretes are examined under the
microscope, it can be seen that they contain quite a large number of air voids that are not similar
to the usual air-entrained bubbles but are mostly irregularly shaped voids due to the compaction
process. Probably in part due to the interconnection of some of the compaction voids, and also
in part to the lower degree of homogeneity the paste, dry concretes generally have a higher
permeability than standard mixtures. The stiff consistency of dry concretes also generally makes
air entrainment extremely difficult. As will be seen in the following paragraphs, the possibility of
22
entraining air bubbles in dry concretes under the requirements of full-scale production remains
an open question.
The necessity of entraining air bubbles in dry concrete mixtures for adequate frost protection is
another controversial question. Although most mixtures are generally proportioned to achieve an
optimum density at the end of the consolidation operations, as just mentioned, most dry concrete
elements are characterized by the numerous irregularly shaped voids formed during the
compaction process. As will be discussed in the following paragraphs, the role played by these
“compaction voids” in the protection against frost remains an open question. While some field
observations and laboratory investigations tend to indicate that some compaction voids can act as
air bubbles and are sufficient to offer an adequate protection against frost-induced internal
cracking, other reports have demonstrated that non-air-entrained dry concretes can be frost
susceptible.
4.2 Air Entrainment in Dry Concrete Mixtures
Air entrainment in concrete concern the production of a network of small spherical air bubbles
that are often called air voids. The role of the air voids that can be entrained in the paste is to
reduce the distance that water must travel to reach an external surface (or an internal freezing
site), and thus the pressure generated by the flow of the water is reduced, hence frost durability is
achieved with sufficient air voids.
The basic parameter concerning frost resistance is the distance between the air voids. There
exists, for any given concrete subjected to a given type of freezing and thawing test, a critical
value of the air void spacing factor. The spacing factor represents very approximately the
average half-distance between two air void walls, and, in ordinary concrete mixtures, is
determined according to ASTM Standard C457 (described in a further section in this document).
23
The critical factor is such that specimens with a higher value will be rapidly damaged by freezing
and thawing cycles, whereas specimens with a lower value will withstand without any significant
damage a very large number of cycles. Figure 9 shows, as an example, the results of a series of
tests made on a conventional concrete with a water/cement ratio of 0,5 [24]. In this series of
tests, the critical value of the spacing factor is approximately 500 µm. It is important to note that
the critical value that is determined only applies for the test that is being used and to the concrete
tested.
0 200 400 600 8000
1000
2000
3000
4000
Air-void spacing factor, L (µm)
Leng
th c
hang
e af
ter 3
00 fr
eezi
ng
and
thaw
ing
cycl
es (µ
m/m
)
Durable Non-Durable
Lcrit - 500 µm
Figure 9 — Internal damage due to frost attack: critical spacing factor [24]
When an ordinary concrete mixture has to be air-entrained, usually just enough air-entraining
agent is used to obtain an adequate spacing factor. Air-entraining agents are generally surface-
active agents (surfactants) that concentrate at the air-water interface in the paste [38]. They are
made of long organic molecules and usually facilitate air entrainment by reducing surface
tension, but they mostly act to stabilize the air bubbles that are entrained by the mixing process.
24
Most air-entraining molecules (which have a hydrophobic and a hydrophilic end) are electrically
charged and this explains in good part their stabilizing influence, because the air bubbles become
attached to the clinker grains (see Figure 10) [39]. Vinsol resin, which is commonly used as an
air-entraining agent in North America, reacts with the lime liberated by the hydration of cement
to form a water-repellent membrane around the air voids [40]. The stabilization process allows
the smaller voids (which are necessary to obtain a low spacing factor) to stay in the mixture
because the natural tendency of air bubbles is to coalesce since this reduces the free energy [41].
AirAir
Air +++
+
+
+
+
++
++++
+ + +++++ +
++
+
C
C
C
Figure 10 - Illustration of the action of air entraining agents
in the formation of spherical air bubbles [39].
Many parameters of course affect the process of air void production in concrete. Generally
speaking, all parameters that affect the viscosity of the paste (such as for instance aggregate
grading, water content, water/cement ratio, water reducing admixtures etc.) also affect air
entrainment. For usual concrete mixtures, as the viscosity increases, the dosage of the air-
25
entraining agent must be increased because air entrainment in a stiffer paste requires more
energy. However, over a certain viscosity (or below a certain water/cement ratio), the concrete
mixture becomes too stiff. The energy requirement for air void production is then too high for
the stirring capacity of most concrete mixers [41, 42].
As explained by Powers [41], the formation of an air bubble is only possible if a sufficient
amount of water is available. For an air-entraining agent to be efficient, there must be enough
water to form a film around each bubble. When the quantity of water added to the mixture is
significantly decreased, water tends, first of all, to cover solid surfaces. There is thus a fight for
water between the bubbles and the solid particles. Below a certain water content, the efficiency
of the air-entraining agent is thus minimized, even at fairly large dosages.
The water content of most dry concrete mixtures is usually of the order of the minimum quantity
required to entrain spherical air bubbles. This is why most researchers agree that the addition of
an air-entraining agent in such concretes usually has very little effect if the batching sequences
commonly used in the industry are not significantly modified. A survey of existing RCC
pavements carried out by the U.S. CORPS of ENGINEERS has indicated that the use of an air-
entraining agent was not very efficient, even at large dosages [43]. As part of the same
investigation, additional tests carried out in two different laboratories showed that “the effect of
the air-entraining admixture on the concrete air void system was somewhat ambiguous”. Several
studies devoted to the improvement of the frost durability of precast dry concrete elements, such
as paving flags, blocks and street curbs, have also underlined the difficulties of entraining and
properly dispersing spherical air bubbles in this kind of mixtures under full-scale production
conditions [44-46].
According to the available information, it seems that the only possible way to entrain spherical
air bubbles is to alter the batching sequence. Andersson [47], for instance, states that attempts to
entrain air in RCC mixtures can be successful if the air-entraining agent is premixed with the
26
cement paste, a small portion of the coarse aggregate and a superplasticizer before adding the
sand. Similar results were obtained, in the laboratory, by Gomez-Dominguez [48], and by
Horrigmoe and Brox Rindal [49]. The authors of both investigations report that a significant
number of spherical air bubbles can be entrained in RCC mixtures when the paste fraction is
premixed with the admixture in a counter-current pan mixer.
Such a procedure is unfortunately of limited application in practice, particularly for RCC
production. Premixing operations require concrete to be mixed in a stationary plant while most
RCC producers use continuous pugmill mixers. This technique is also unattractive to most
precast concrete producers, because it tends to decrease considerably the production efficiency.
Finally, it appears that this procedure is not necessarily bound to success and could be the source
of another kind of problem. A laboratory investigation, conducted some years ago by the
PORTLAND CEMENT ASSOCIATION [50], indicates that the type of air-entraining agent and the
dosages added to the mixture have a strong influence on the final result. While studying the
possibility of air entrainment in no slump concrete (i.e. with a consistency just a little less stiff
that of usual dry concrete mixtures), Whiting found that the amount of admixture required to
entrain spherical air bubbles could be ten times superior to that used in conventional concretes.
His results also demonstrate that it was outright impossible to entrain any air bubbles with some
air-entraining agents even when large dosages were used. Furthermore, Whiting emphasized in
his report that the required dosages of air entraining agent can affect the consistency of the
concrete mixture by significantly increasing the slump.
These results were later confirmed by a study by Marchand et al. [51] who investigated the effect
of air entrainment on the air-void characteristics of twenty-one different concrete mixtures
produced in the laboratory. Four different air-entraining admixtures (AEA), selected for their
different chemical compositions, were tested. Test variables included admixture dosage and type
of mixer. The air-void characteristics of each mixture were measured in accordance with the
ASTM C457 standard procedure. A pressure-saturation test was also used to measure the amount
27
of "unconnected voids" for each concrete mixture. Finally, samples from nine selected mixtures
were polished and observed using a scanning electron microscope (SEM) to see if the addition of
an AEA had contributed to entrain microscopic bubbles. The results of the microscopical
examinations indicated that bubble-type air voids were entrained in significant numbers in only
two mixtures, even if fairly large dosages of various air-entraining admixtures were used. Both
of these mixtures were prepared in a counter-current pan mixer. For eight air-entrained mixtures,
numerous microscopic air bubbles were observed during the SEM observations while few larger
air bubbles were detected during the optical measurements. The discrepancy between these two
series of results could not be explained. Numerous irregularly shaped voids, resulting from the
compaction operations, were also observed in the paste, and the value of the spacing factor
(according to ASTM C457) for most mixtures was found to be smaller than 250 µm (0.01 in.).
Even if most of these investigations indicate that air entrainment in dry concretes is extremely
difficult under the usual conditions of industrial production, the recent introduction of more
powerful air-entraining admixtures especially designed for this type of concrete has raised new
hopes. More research is certainly needed to determine if these admixtures can consistently
produce an adequate air bubble network without adversely affecting the required workability of
the mixture. Encouraging results of successful air entrainment in RCC mixtures have been
recently reported by Ragan [52] and Dolen [53]. Hazrati et al. [54] also obtained good results for
concrete mixtures used in the production of masonry units. In all cases, a significant amount of
spherical air bubbles could be entrained with, apparently, no alteration of the batching sequence.
4.3 The Frost Durability of Dry Concretes
As mentioned earlier, if the possibility of entraining spherical air bubbles in dry concrete
mixtures remains an unresolved question, the necessity of air entrainment for an adequate frost
protection is also the subject of a controversy. Much of this originates from the fact that only a
28
few investigations have been carried out up to now to evaluate the frost durability of dry concrete
elements (particularly on the behavior of concrete masonry units). Although a great deal of effort
has been made lately towards designing new test procedures to evaluate the frost resistance of
these materials, insufficient laboratory data are presently available, and reports on field
performance remain unfortunately extremely limited.
Dry concretes, like all other types of concrete elements, can be subjected to two types of
deterioration due to frost: internal cracking and surface scaling. In service, each type of damage
can occur separately, but, in some cases, certain concrete elements can be exposed to the
combined action of the two aggressions. It is then difficult to distinguish between the two effects.
As will be seen later, this seems to be particularly the case with paving blocks. However, since
most authors agree to make a clear distinction between the two aggressions, and considering that
most laboratory test procedures are designed to independently study each mechanism,
information concerning these two phenomena will be presented separately.
4.3.1 The Ability of Dry Concretes to Resist Internal Microcracking
Internal microcracking is generally associated to the bulk destruction of a water saturated
concrete element submitted to repeated cycles of freezing and thawing. As discussed in section
3.2, a number of experimental procedures (such as ASTM C666 and ASTM C1262) can be used
to investigate the ability of dry concrete mixtures to resist frost-induced microcracking. The use
of different methods can partly explain the apparent contradiction between the results reported
by various authors.
It should be emphasized that there is very little published data on the freezing and thawing
durability of dry concrete elements. This is the reason why this portion of the review is not solely
limited to the performance of concrete masonry units. This sub-section also contains information
29
on the frost behavior of other types of dry concrete mixtures such as RCC and precast paving
blocks.
As part of a laboratory study of the engineering properties of RCC pavements, Gomez-
Dominguez [48] analyzed the influence of air entrainment on frost durability. Several concrete
mixtures were prepared with various water/cement ratios (w/c = 0.30, 0.40 and 0.50), and with
and without an air-entraining admixture. The efficiency of three air-entraining agents was
assessed. The mixing sequence was altered in order to entrain spherical air bubbles. The frost
durability of all concrete mixtures was determined after 28 days of water curing in accordance
with ASTM C666. Some mixtures were tested according to Procedure A (freezing in water)
while others were tested according to Procedure B (freezing in air). The deterioration of the
specimens was evaluated by monitoring the pulse velocity changes during the test. Although all
mixtures showed significant signs of deterioration (none had a durability factor over 70% after
300 freezing and thawing cycles), the author concluded that the addition of an air-entraining
agent had a positive effect since the air entrained mixtures withstood without damage more
cycles of freezing and thawing than their companion plain mixtures. Test results indicated no
significant influence of the type of air-entraining admixture.
In a survey conducted for the U.S. CORPS of ENGINEERS, Ragan [43] has recently studied the
frost durability of nine roller-compacted concrete pavements made between 1976 and 1985.
Specimens were sawn from the various pavements and subjected to freezing and thawing cycles
in accordance with ASTM C666, procedure A. The frost durability of two series of laboratory-
fabricated specimens was also investigated according to the same test method. In all cases, the
air void characteristics of the different concrete mixtures were determined in accordance with
ASTM C457 - Microscopical determination of air-void content and parameters of the air-void
system in hardened concrete. The main conclusion of this study was that the frost durability of
RCC is directly related to the air void spacing factor. As shown in Figure 11, the value required
for good concrete durability was evaluated to be approximately 250 µm (0.01 in.). Since an air-
30
entraining agent had been added to only two of these mixtures and because no significant amount
of air bubbles were found during the microscopical examinations, the results of these tests tend
to indicate that the compaction air void system in dry concretes can offer the same protection
against frost action than entrained air voids. Following a laboratory investigation of the influence
of two Canadian fly ashes on the engineering properties of lean RCC mixtures, Joshi and Natt
reached similar conclusions [55]. Their test results showed that non air-entrained RCC can be, to
a certain degree, resistant to frost-induced microcracking.
�
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������������� �
����������� �����
� ��
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Figure 11 — Durability factor versus the spacing factor (according to reference 43)
Field performance observations of existing pavements built in British-Colombia and in
New-Hampshire, respectively reported by Piggott [56], and by Hutchinson et al. [57], have also
indicated that non-air-entrained RCC can be resistant to frost induced internal cracking. These
conclusions should however be considered with caution since most of these pavements were
exposed to a mild frost attack for only a limited period. Moreover, the validity of some of these
observations has been recently questioned by the test results reported by Ragan [43], since the
samples taken from one of the British Colombia pavements showed poor durability when tested
in the laboratory according to ASTM C666. The samples used in Ragan's laboratory
investigation were approximately eight years old at the time of testing and, according to Piggott,
31
had showed no sign of deterioration in service. Further discrepancies between laboratory test
results and field performance of RCC structures have recently been reported by Liu [58] of the
U.S. CORPS of ENGINEERS.
Part of the discrepancy between laboratory test results and field observations can be attributed to
the relative severity of the ASTM C666 [59, 60]. This conclusion is supported by the data of
Pigeon and Marchand [7] and those of Marchand et al. [61]. These authors studied the frost
durability of more than 40 different RCC mixtures tested in the laboratory and exposed in service
to severe freezing conditions. ASTM C666 tests performed on non air-entrained samples cored in
existing pavements (after more than 28 days of curing) and brought back in the laboratory
showed that parameters such as water/binder ratios and type of binder had very little influence on
the resistance of RCC to internal microcracking. Test results also emphasized the marked effect
of air entrainment and degree of consolidation on the performance of RCC. On the one hand, the
entrainment of small volume of spherical air bubbles (around 2%) was found to have a very
beneficial influence on the behavior of RCC. On the other hand, badly consolidated mixtures
(with a degree of compaction inferior to 98%) were all found to be frost susceptible when tested
according to the ASTM C666 procedure. However, a systematic evaluation of the condition of
the test sections after more than ten years of exposure to Canadian winters showed no signs of
degradation even for the low-density (badly compacted) mixtures [62].
Globally, most of the reports on the subject tend to indicate that air entrainment (when possible)
is not solely beneficial to RCC mixtures but can also improve the behavior of other types of dry
concrete mixtures. For instance, freezing and thawing tests performed by Hazrati et al. [54]
according to the ASTM C1262 clearly showed that the entrainment of a small volume of air
could significantly enhance the durability of masonry units. These results are also in good
agreement with the observations of Shideler and Toennies [63].
32
Test results on the influence of air entrainment on the frost durability of dry concrete mixtures to
should be considered with caution [64]. On the one hand, the addition of an air-entraining
admixture (even at fairly large dosages) does not automatically result in the production of a
significant number of air bubbles. On the other hand, the addition of an air-entraining admixture
has been found to result of very small air bubbles not visible under an optical microscope [51]
(see previous section). In that respect, it is unfortunate that researchers do not systematically
attempt to verify the presence of spherical bubbles in mixtures produced with an air-entraining
agent.
It should also be emphasized that, in many of the investigations where air entrainment was not
found to have any influence on the frost durability of concrete, the material was already well
protected by the presence of non-connected compaction voids [65, 66]. The beneficial influence
of non-connected voids has been confirmed by many authors [7, 64, 67, 68].
The application of ASTM Standard C457 to dry concretes should be also examined in depth. It is
certainly not obvious that this procedure, originally developed for normal concretes with well-
dispersed spherical air bubbles, is applicable to dry concretes mainly characterized by large
irregularly shaped compaction air voids. Moreover, despite the test data reported by Ragan [43],
it is possible that the usual relationship between spacing factor and freezing and thawing
durability does not apply to all dry concrete mixtures. The usual limit of 250 µm (0.01 in.) refers
to a particular air void distribution, and it is possible that the presence of numerous irregularly
shaped air voids could affect this value.
Data on the influence of mixture designs on the durability of precast dry concrete elements (such
as masonry units and pavers) tend to be contradictory. For instance, Boisvert et al. [65] reported
that variables such as type of binder, water/binder ratio had very little influence on the resistance
of concrete pavers to frost-induced microcracking. All samples were tested according to ASTM
C666. These observations are in good agreement with those of Redmond [69] who found that the
33
type of binder had very little influence on the performance of masonry units. However, these
conclusions are in apparent contradiction with those of Pfeiffenberger et al. [70]. Using a
modified version of ASTM C67, these authors found that the frost durability of masonry units
could be enhanced by the use of silica fume.
Globally, most studies clearly emphasize the importance of selecting frost durable aggregates.
This is for instance the case for Embacher et al. [71] who studied the influence of different
parameters affecting the field performance of masonry units. Their visual inspections of various
structures revealed that the poor durability of certain segmental retaining wall units was most
likely due to improper mixture designs and/or the use of non-durable aggregates. The importance
of a good aggregate selection was also emphasized by the study of Bowzer et al. [72].
As for RCC mixtures, many investigations also indicate that the production of dense and well-
consolidated mixtures directly contributes to improve the frost durability of masonry units. These
properties can usually be achieved by a proper mixture design (that will control the packing
density of the aggregates and the water/binder ratio of the mixture) and appropriate compaction
of the units during production. Although there appears to be conflicting results on the
relationship between the water absorption of masonry units and their frost durability,
compressive strength is usually found to correlate well with their resistance to frost-induced
internal microcracking.
For instance, Schoenfeld [73] used the ASTM C666 test procedure to study the frost resistance of
concrete pavers. The properties (absorption, dimensions, unit weight and compressive strength)
of the units were made according to ASTM C140. According to the author, compressive strength
is a good indicator of the frost resistance of precast dry concrete elements. This conclusion is in
good agreement with the results of other authors [68, 74, 75]. According to MacDonald et al.
[68], a compressive strength of 45 MPa appears to be a critical value above which frost
durability is ensured. It should, however, be kept in mind that good mechanical performance is
34
obtained by reducing the water/binder ratio of the mixture and by controlling consolidation
operations at the plant.
4.3.2 The Deicer Salt Scaling Resistance of Dry Concrete Elements
In most Nordic countries, the chloride-induced deterioration of many concrete structures is a
matter of growing concern. The widespread use of deicing salts during winter roadway
maintenance operations is known to significantly increase damage caused by repeated freezing
and thawing cycles. Since most dry concrete products are used for roadway applications, their
deicer salt scaling resistance is of paramount importance, and is often considered by many
potential users as an acceptability criterion.
Aware of the importance of a good deicer salt scaling resistance and also of the practical
difficulties of entraining air in dry concrete mixtures, many investigators have tried to establish
materials criteria and production procedures to assist producers. To our knowledge, Schubert and
Lühr [76, 77] were the first to specifically study the deicer salt resistance of dry concrete
products. As part of a research program aimed at finding laboratory test procedures that could be
used by producers to evaluate the service life of their products, they tested several non air-
entrained precast concrete products, mainly paving blocks, from various productions. The
laboratory results were compared to the performance of selected specimens exposed to natural
field conditions over a six-year period. They found that it was extremely difficult to accurately
evaluate the durability of the paving blocks since all productions were plagued by large
variations in the properties of the paving blocks.
A similar conclusion was drawn by Clark [78] after a comparative test series of several non-air-
entrained paving blocks provided by various producers. In their respective studies, Schubert and
Lühr, and Clark used the same type of test to reach these conclusions. All specimens were
35
submitted to daily freezing and thawing cycles with a salt solution on top of the wearing surface.
From these two studies, it appears that the performances of precast paving blocks can be strongly
affected by the casting operations and that special care should be taken to adequately consolidate
all pavers whatever their position in the mould.
Numerous attempts were made to correlate the deicer salt scaling resistance of precast paving
blocks to other properties such as capillary water absorption, density and mechanical strength.
Such correlations could enable a producer to assess the durability of his production without the
long and costly delays associated with the usual testing methods. Schubert and Lühr’s test results
[76, 77] showed a good correlation between the capillary water absorption of paving blocks and
their deicer salt scaling resistance as measured in the laboratory. They found however no
correlation between the mechanical (i.e. compressive and flexural) strength and the durability of
the specimens. It is also interesting to note that the pre-drying of the specimens at 40° C and the
subsequent resaturation with water prior testing had, in most cases, a detrimental influence on
their frost durability.
Schubert and Lühr’s conclusions appear to be in good agreement with the results of Ghafoori et
al. [79]. Using the Canadian standard CAN3-A231.2-M85, these authors observed that a concrete
paving blocks should possess the following characteristics to be deicer salt scaling resistant:
cement content of about 385 kg/m3 (650 lb/yd3), a mean compressive strength of at least 65 MPa
(9700 psi), a mean split tensile strength of about 6 MPa (940 psi), a mean unit weight of about
2275 kg/m3 (142 lb/ft3) and a mean absorption of about 4%. Their results also indicate that the
cement-aggregate ratio strongly influences the frost durability. The authors conclude that
minimum strength requirements, for structural purpose, cannot be solely used to predict the frost
durability of masonry units.
In an investigation of the salt scaling resistance of numerous paving flag productions, Siebel and
Neck [80] could not find any useful correlation between parameters such as the compressive
36
strength, the dynamic modulus of elasticity, and the water capillary absorption, with the
durability of the specimens.
In his study, Clark [79] came to the conclusion that the deicer salt scaling resistance varied
significantly with only one of the parameters analyzed, the water/cement ratio. According to his
data, all mixtures having a water/cement ratio below 0.35 showed a good durability.Aware of the
difficulties encountered by a producer to consistently control the water/cement ratio of his
production, Clark recommended keeping the cement content over a 380 kg/m3 limit. A similar
recommendation was given by Siebel and Neck [80] who suggested that single-layer blocks
should have a minimum cement content of 320 kg/m3 to be durable.
More recent investigations on the deicer salt scaling durability of precast dry concrete elements,
such as masonry units and pavers, have clearly emphasized the beneficial influence of air
entraining spherical air bubbles. According to these studies, it appears that air contents as low as
2% can have a marked positive influence on the performance of dry concrete mixtures [7, 54,
65]. The investigation of Pigeon and Marchand [7] also clearly shows that the beneficial
influence of silica fume.
According to all of these studies, the deicer salt scaling resistance of dry concrete products
appears to be intrinsically linked to reduced porosity (i.e. low volume of freezable water) and
low permeability. All recommendations (low water/cement ratio, good compaction, addition of
mineral additives) are meant to improve the ability of the concrete to resist saturation and limit
ice formation.
However, it is not obvious that these guidelines are sufficient to produce durable dry concrete
elements. Given the limited number of investigations carried out on the subject, many questions
remain unanswered. For instance, the role and the necessity of air entrainment are still not clear.
37
The effect of drying at temperatures close to 40° C (that can be observed at the surface of most
pavements during summer time) on the scaling resistance of dry concretes needs to be
investigated. More research is needed to clearly understand the mechanisms of salt scaling in dry
concretes, and the influence of their internal structure on these mechanisms.
Furthermore, the influence of permeability on the mechanisms of degradation remains unclear.
According to Hazrati and Kerkar [54], it seems that any condition that reduces permeability
without decreasing drastically the porosity could be detrimental to the deicer salt scaling
durability of dry concrete mixtures. For instance, products, like efflorescence-impeding
admixtures, that reduce the permeability of the material under atmospheric pressure have been
found to have a detrimental influence on the performance of dry concrete mixtures.
5 Concluding Remarks
The literature survey indicates that concrete masonry units can be susceptible to freezing and thawing cycles. This appears to be particularly the case for non air-entrained mixtures. The review of the literature also demonstrates that it is possible to produce non air-entrained dry concrete mixtures that are resistant to frost-induced internal microcracking. This tends to indicate that compaction air voids can, under certain circumstances, provide an adequate protection against frost action. This is only possible if the compaction voids, or at least part of them, are not interconnected. In that respect, special attention should be given to materials selection and production operations. The discontinuity of the compaction void network results from an optimum packing density of the aggregate particles and proper consolidation of the fresh mixture during the casting operations.
38
Most reports also tend to demonstrate that dry concrete elements, such as precast masonry units and concrete paving blocks, are usually more susceptible to deicer salt scaling. The use of an air-entraining agent has been found to significantly improve the resistance of dry concrete mixtures to freezing in the presence of a deicer salt solution. Some investigations seem to indicate that only a small volume of spherical air voids (around 2%) is sufficient to improve the frost durability of dry concrete elements (such as precast masonry units). It also seems that the addition of an air-entraining admixture (at fairly large dosages) can result in the entrainment of very small air voids that cannot be seen under the optical microscope at 100X magnification. Notwithstanding the positive influence of the compaction voids for frost resistance, the application of the ASTM C457 test to such a network of voids should be reconsidered. This procedure was developed for normal air-entrained concretes with air bubbles ranging approximately between 10 µm and 1 mm in diameter. As the test results show, the usual limits of 200 or 250 µm (approximately 0.01 in.) for good protection against frost and salt scaling are therefore not generally applicable to mixtures with compaction air voids. The type of binder generally has only a small influence on the deicer salt scaling durability of dry concrete elements. However, similarly to what is seen for RCC, the use of silica fume can have a positive influence on the performance of these mixtures. Many reports tend to indicate the existence of a minimum cement content (or a maximum water/cement ratio) to ensure the frost durability of dry concrete mixtures. However, critical values vary from one author to another. Chemical admixtures, other than air-entraining agents, appear to have little influence on the frost durability of dry concrete mixtures (such as masonry units and concrete paving blocks). However, products, like efflorescence-impeding admixtures, that reduce the permeability of the material under atmospheric pressure have been found to have a detrimental influence on the performance of dry concrete mixtures.
39
In the literature, numerous attempts have been made to correlate the frost durability (i.e. resistance to internal microcracking and deicer salt scaling) of precast dry concrete elements to other properties such as water absorption, density and mechanical strength. Results on the subject are contradictory indicating that there is no simple relationship between the performance of these mixtures and their physical properties.
6 Recommendations
The literature survey indicates that air entrainment in dry concrete mixtures remains a controversial subject. More research is needed to investigate the necessity of entraining spherical air voids in precast masonry units. More work should also be devoted to the mechanisms of air entrainment in dry concrete mixtures. Although dry concrete mixtures are generally proportioned to achieve an optimum density, most
elements are characterized by numerous irregularly shaped voids formed during the
consolidation process. The role played by these "compaction voids" in the protection against
frost should be elucidated. While some field observations and laboratory investigations tend to
indicate that some compaction voids act as air bubbles and are sufficient to offer an adequate
protection against frost deterioration, other reports have demonstrated that non-air-entrained no-
slump concretes can be frost susceptible.
As previously mentioned, many published reports found in the literature tend to indicate that
there is no direct correlation between the performance of these mixtures and their physical
properties (absorption, mechanical strength, …). More work is thus required to investigate the
relationship between frost durability (i.e. resistance to internal microcraking and deicer salt
scaling) of masonry units and more relevant (and readily available) parameters such total
porosity, volume of non-connected voids and water/binder ratio.
40
More work is needed to determine the reliability of standard test procedures (such as ASTM
C1262). There exists actually very little information on the ability of these tests to discriminate
between durable mixtures and those that will suffer from premature degradation under “natural”
exposure conditions. In many cases, standard test procedures have been found to be too severe.
The reliability of the standard test methods could be established by systematically comparing the
field performance of masonry units (of known compositions) to their durability as determined
according to the various procedures.
This work should also provide interesting information on the critical limits of degradation to be
respected for each test procedure. Actual guidelines do not appear to be based on any systematic
evaluation of the field performance of masonry units.
This analysis should take into consideration the intrinsic variability of commercially produced
masonry units. Very little information is available on the subject. The few published data tend to
indicate that the physical properties of dry concrete elements can vary significantly within a
single production.
41
7 References
[1] Pigeon M. and Pleau R. (1995), Durability of concrete in cold climates, Modern Concrete
Technology 4, Chapman and Hall, 244 p. [2] Marchand J., Hornain, H., Diamond, S., Pigeon, M. and Guiraud, H. (1996), The
Microstructure of Dry Concrete Products, Cement and Concrete Research, Vol. 26, N° 3, pp. 427-438.
[3] British Cement Association, Freeze/thaw Durability of Concrete Block Paving – Final
Technical Report, February 1993, p. 83. [4] Bilgeri P. (1984), Concrete Paving Blocks, Concrete Precasting Plant and Technology,
N° 3, pp. 161-166. [5] Walton R.W. (1991), The North American Precast Concrete Industry, Concrete
Precasting Plant and Technology, N° 1, pp. 34-36. [6] Schackel B. (1987), Beton-Verbundsteinpflaster, Concrete Precasting Plant and
Technology, N° 8, pp. 541-547. [7] Pigeon, M. and Marchand, J. (1993), The Frost Durability of Dry Concrete Products,
Report GCS-93-06, CRIB-Civil Engineering Department, Laval University, Canada. [8] Powers T.C. (1949), The Air Requirement of Frost Resistant Concrete, Proceedings of the
Highway Research Board, Vol. 29, pp. 184-211. [9] Powers T.C. and Helmuth R.A. (1953), Theory of Volume Changes in Hardened Cement
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42
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43
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44
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45
[48] Gomez-Dominguez J. (1987), Roller-Compacted Concrete for Highway Applications, Ph.
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46
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47
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48
[82] Marchand J., Boisvert J., Pigeon M. and Isabelle H.L. (1992), Deicer Salt Scaling
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STUDY ON IMPROVING THE FROST DURABILITY OF CONCRETE MASONRY PRODUCTS
Final Report
Task 2 - Test Results
Project SEM00110
Presented to :
Mr. Robert D. Thomas National Concrete Masonry Association
2302 Horse Pen Road Herndon, VA 20171-3499
USA
January 2004
2
THE STUDY - OBJECTIVES AND PROJECT DESCRIPTION The main objective of the Study is to develop an index that could be used to predict the frost durability of masonry units. This frost durability index, based on the determination of the total volume of disconnected (unsaturated) voids and on an estimation of the volume of freezable water, could be used by producers to improve the durability of their products. The elaboration of this index could also help to establish acceptable limits for the degradation of masonry units tested in tap water and in a salt solution. The Study is divided in the following six different tasks. • Task 1 - Literature Survey; • Task 2 - Data Analysis and Additional Testing; • Task 3 - Elaboration of a Frost Durability Index; • Task 4 - Laboratory Validation of the Durability Index; • Task 5 - Assessment of the Frost Durability of Existing Masonry Units; • Task 6 - Reporting. Each of the first five tasks addresses a specific aspect of the problem. The last task is devoted to the preparation of the progress and final reports. The project was elaborated in such a way to optimize the use of existing data of the frost durability of masonry units generated by the National Concrete Masonry Association (NCMA). The first part of the Study, a comprehensive search of the scientific and technical literature, was conducted in 2001. The final report for Task 1 was submitted to NCMA on August 7, 2001. This report presents the test results and our final data analysis for Task 2 of the Study. In order to complete this report, additional data on the composition of the mixture needs to be gathered. Task 2. Background Information The Study was to be mainly based on a series of readily available parameters provided by a previous study conducted by NCMA in 1998-1999 [1]. In this research, a total of 30 sets of concrete masonry units were manufactured and tested to help determine the effect of various production parameters on their freeze-thaw durability. Test variables included type of cement (Type I and Type III), mixture water content, use of various chemical admixtures (water repellent, plasticizer), aggregate gradation and compaction
3
time. The compressive strength, absorption and density of all mixtures were determined. Frost durability tests were also carried out (in tap water and salt solution) according to the requirements of ASTM C1262. Samples of each concrete mixture of the various sources of aggregates used in the project had been set aside for testing. In 2001, NCMA decided that the current Study would not be based on results provided by the 1998-1999 study but on test results from an entire new set of units. A total of 31 different mixtures were manufactured in April 2002 by two different producers. Many test variables were also modified. All changes made to the original research program as well as the information regarding the production of these new units are provided in a subsequent section of the progress report (see New Mixture Composition). For all 31 concrete mixtures, their compressive strength, absorption and density were determined by the NCMA Research & Development Laboratory. Frost durability tests were also carried out by NCMA (in tap water and salt solution) according to the requirements of ASTM C1262. Results were made available in January 2003. Task 2. New Mixture Composition As previously mentioned, a total of 31 different mixtures were produced by two different producers (Set A and Set B). Set A, provided from 16 different production runs, consisted of specimens from Versa-Lok retaining wall units produced by Barnes and Cone of Syracuse (New York). Set B were taken from 4x8x16 concrete masonry units made at the World Concrete Center in Alpena (Michigan) from 15 different production runs. The new test variables are the following: - mixture water content; - cement content; - aggregate gradation; - use and dosage of various chemical admixtures (air entraining, FT enhancer, calcium
stearate dispersing admixtures). A Type I cement was used for both sets of specimens. The cement was supplied by two different manufacturers. The use of a Type III cement is no longer a variable in the Study. Furthermore, the compaction time of all mixtures was not monitored during production.
4
The mixture composition for specimens from Set A are given in Tables 1a and 1b and the mixture composition for specimens from Set B are given in Tables 2a and 2b. This information was obtained in a report submitted by NCMA on January 27th, 2003. A code identification (ID) was developed to identify each mixture with respect to its composition and origin. The first number of the mixture codification indicates the quantity of cement used in the mixture (%). The following letter identifies the producer (Producer A or Producer B). In this report, it is also referred to Set A (Producer A) and Set B (Producer B). The remaining letters are all related to the constituent. The definition of all abbreviations used are:
• HW for high water content; • ECA indicates the use of an Efflorescence Control Admixture; • NA indicates that no air entraining admixture was used; • LA means that a low dosage of air-entraining admixture was used; • HA means that a high dosage of air-entraining admixture was used; • FT indicates the use of a freeze-thaw enhancer admixture; • Ref refers to the reference mixture (without any admixture).
For instance, 16-A-ECA-LA-FT means that the mixture contains 16% of cement (mass). It was produced by Producer A. The mixture contains also an Efflorescence Control Admixture (ECA), an air-entraining admixture at a low dosage (LA) and a quantity of a freeze-thaw enhancer admixture (FT). In Tables 1 and 2, ECA stands for efflorescence control admixture. ECA is for calcium stearate dispersing admixture which has been used in the industry as the raw material for most integral water repellent admixtures. FT Enhancer is an admixture marketed as one to improve freeze-thaw durability (it is not an air entraining admixture).
5
Table 1a - Mixture Composition (SET A) (in lbs)
ID Mix Description Admixture(s) Dosage oz./cwt.
Sand lbs/cyc
Limestone Dust
lbs/cyc
Gravel lbs/cyc
Cement lbs/cyc
Water Added
gal
13-A-Ref Normal Water, 13% Cmt Plasticizer 4 1739 865 680 408 1.6
10-A-HW High Water, 10% Cmt Plasticizer 4 1739 867 690 312 3.3
13-A-HW High Water, 13% Cmt Plasticizer 4 1735 869 680 408 4.5
16-A-HW High Water, 16% Cmt Plasticizer 4 1737 869 690 502 4.7
10-A-ECA-NA ECA, 10% Cmt ECA 12 1737 868 690 316 2.9
13-A-ECA-NA ECA, 13% Cmt ECA 12 1735 861 690 408 4.7
16-A-ECA-NA ECA, 16% Cmt ECA 12 1735 857 680 506 5.7
10-A-ECA-NA-FT FT Enhancer (no air), 10% Cmt
FT Enhancing + ECA
4.5 + 12 1737 859 690 316 3.0
13-A-ECA-NA-FT FT Enhancer (no air), 13% Cmt
FT Enhancing + ECA
4.5 + 12 1735 862 680 410 4.5
16-A-ECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing + ECA
4.5 + 12 1735 862 690 504 5.0
10-A-ECA-LA-FT FT Enhancer (low air), 10% Cmt
A/E - Low Dose + ECA
7 + 12 1703 860 710 312 10.4
13-A-ECA-LA-FT FT Enhancer (low air), 13% Cmt
A/E - Low Dose + ECA
7 + 12 1701 861 690 408 9.1
16-A-ECA-LA-FT FT Enhancer (low air), 16% Cmt
A/E - Low Dose + ECA
7 + 12 1729 868 700 502 4.9
13-A-ECA-HA-FT FT Enhancer (high air), 13% Cmt
A/E - High Dose + ECA
14 + 12 1734 871 690 414 1.0
16-A-ECA-HA-FT FT Enhancer (high air), 16% Cmt
A/E - High Dose + ECA
14 + 12 1737 865 700 510 3.1
16-A-2xECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing +ECA + ECA
14 + 12+12
2594 1285 1000 674 -
6
Table 1b - Mixture Composition (SET A) (in %)
ID Mix Description Admixture(s) Dosage oz./cwt.
Sand (%)1
Limestone (%)1
Gravel (%)1
Cement (%)2
Water Added
gal
13-A-Ref Normal Water, 13% Cmt Plasticizer 4 53 26 21 12 1.6
10-A-HW High Water, 10% Cmt Plasticizer 4 53
26 21 9 3.3
13-A-HW High Water, 13% Cmt Plasticizer 4 53 26 21 12 4.5
16-A-HW High Water, 16% Cmt Plasticizer 4 53 26 21 15 4.7
10-A-ECA-NA ECA, 10% Cmt ECA 12 53 26 21 10 2.9
13-A-ECA-NA ECA, 13% Cmt ECA 12 53 26 21 12 4.7
16-A-ECA-NA ECA, 16% Cmt ECA 12 53 26 21 15 5.7
10-A-ECA-NA-FT FT Enhancer (no air), 10% Cmt
FT Enhancing + ECA
4.5 + 12 53 26 21 10 3.0
13-A-ECA-NA-FT FT Enhancer (no air), 13% Cmt
FT Enhancing + ECA
4.5 + 12 53 26 21 13 4.5
16-A-ECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing + ECA
4.5 + 12 53 26 21 15 5.0
10-A-ECA-LA-FT FT Enhancer (low air), 10% Cmt
A/E – Low Dose + ECA
7 + 12 52 26 21 10 10.4
13-A-ECA-LA-FT FT Enhancer (low air), 13% Cmt
A/E – Low Dose + ECA
7 + 12 52 26 21 13 9.1
16-A-ECA-LA-FT FT Enhancer (low air), 16% Cmt
A/E – Low Dose + ECA
7 + 12 52 26 21 15 4.9
13-A-ECA-HA-FT FT Enhancer (high air), 13% Cmt
A/E – High Dose + ECA
14 + 12 53 26 21 13 1.0
16-A-ECA-HA-FT FT Enhancer (high air), 16% Cmt
A/E – High Dose + ECA
14 + 12 53 26 21 15 3.1
16-A-2xECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing +ECA + ECA
14 + 12+12
53 26 20 14 -
1 expressed as a percentage of the total mass of aggregates (example for sand, % sand = (mass of sand / (mass of sand + mass of limestone + mass of gravel)). 2 % cement = mass of cement / (mass of sand + mass of limestone + mass of gravel).
7
Table 2a - Mixture Composition (SET B) (in lbs)
ID Mix Description Admixture(s) Dosage oz./cwt.
2NS Sand lbs/cyc
29A Stone lbs/cyc
Cement lbs/cyc
Water Added
gal
13-B-Ref Normal Water, 13% Cmt Plasticizer 3 1388 603 270 9.4
10-B-HW High Water, 10% Cmt Plasticizer 3 1428 578 196 9.5
13-B-HW High Water, 13% Cmt Plasticizer 3 1397 608 269 -
16-B-HW High Water, 16% Cmt Plasticizer 3 1465 536 318 -
10-B-ECA-NA ECA, 10% Cmt ECA 6 1395 604 202 4.6
13-B-ECA-NA ECA, 13% Cmt ECA 6 1400 601 256 6.4
16-B-ECA-NA ECA, 16% Cmt ECA 6 1397 601 319 6.5
10-B-ECA-NA-FT FT Enhancer (no air), 10% Cmt
FT Enhancing + Plast
25 + 4 1410 591 207 3.3
13-B-ECA-NA-FT FT Enhancer (no air), 13% Cmt
FT Enhancing + Plast
25 + 4 1432 576 256 5.5
16-B-ECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing + Plast
25 + 4 1395 613 324 6
10-B-ECA-LA-FT FT Enhancer (low air), 10% Cmt
A/E - Low Dose + Plast
7 + 6 1486 521 198 3.9
13-B-ECA-LA-FT FT Enhancer (low air), 13% Cmt
A/E - Low Dose + ECA
7 + 6 1401 609 260 5.5
16-B-ECA-LA-FT FT Enhancer (low air), 16% Cmt
A/E - Low Dose + ECA
7 + 6 1399 619 320 6.5
13-B-ECA-HA-FT FT Enhancer (high air), 13% Cmt
A/E - High Dose + ECA
14 + 6 1399 701 260 4
16-B-ECA-HA-FT FT Enhancer (high air), 16% Cmt
A/E - High Dose + ECA
14 + 6 1398 594 320 5.2
8
Table 2b - Mixture Composition (SET B) (in %)
ID Mix Description Admixture(s) Dosage oz./cwt.
2NS Sand (%)1
29A Stone (%)1
Cement (%)2
Water Added
gal
13-B-Ref Normal Water, 13% Cmt Plasticizer 3 70 30 14 9.4
10-B-HW High Water, 10% Cmt Plasticizer 3 71 29 10 9.5
13-B-HW High Water, 13% Cmt Plasticizer 3 70 30 13 -
16-B-HW High Water, 16% Cmt Plasticizer 3 73 27 16 -
10-B-ECA-NA ECA, 10% Cmt ECA 6 70 30 10 4.6
13-B-ECA-NA ECA, 13% Cmt ECA 6 70 30 13 6.4
16-B-ECA-NA ECA, 16% Cmt ECA 6 70 30 16 6.5
10-B-ECA-NA-FT FT Enhancer (no air), 10% Cmt
FT Enhancing + Plast
25 + 4 70 30 10 3.3
13-B-ECA-NA-FT FT Enhancer (no air), 13% Cmt
FT Enhancing + Plast
25 + 4 71 29 13 5.5
16-B-ECA-NA-FT FT Enhancer (no air), 16% Cmt
FT Enhancing + Plast
25 + 4 69 31 16 6
10-B-ECA-LA-FT FT Enhancer (low air), 10% Cmt
A/E - Low Dose + Plast
7 + 6 74 26 10 3.9
13-B-ECA-LA-FT FT Enhancer (low air), 13% Cmt
A/E - Low Dose + ECA
7 + 6 70 30 13 5.5
16-B-ECA-LA-FT FT Enhancer (low air), 16% Cmt
A/E - Low Dose + ECA
7 + 6 69 31 16 6.5
13-B-ECA-HA-FT FT Enhancer (high air), 13% Cmt
A/E - High Dose + ECA
14 + 6 67 33 12 4
16-B-ECA-HA-FT FT Enhancer (high air), 16% Cmt
A/E - High Dose + ECA
14 + 6 70 30 16 5.2
1 expressed as a percentage of the total mass of aggregates (example for sand, % sand = (mass of sand / (mass of sand + mass of limestone + mass of gravel)). 2 % cement = mass of cement / (mass of sand + mass of stone). Task 2 . Objectives and Testing Program The first objective of Task 2 was to provide additional data on the properties of the various mixtures produced by NCMA. In order to fully understand the mechanisms that control the frost durability of masonry units, it is important to characterize the air void network of these mixtures. To do so, standardized and non-standardized tests were carried out on samples of selected mixtures. To determine the role played by compaction voids and spherical air bubbles in the protection against frost, the following tests on selected units were carried out:
9
- Measurement of air-void system characteristics according to a modified ASTM C457; - Pressure-saturation tests; - Micro air-void analyses by scanning electron microscopy. To determine the characteristics of the air-void system, two polished 100x100-mm (4x4-inches) sections from each mixture were microscopically examined in accordance with the modified point count method described in the ASTM C457 Standard – Standard Test Method for the Determination of the Air Content and the Air-Void Characteristics of Hardened Concrete. The characteristics of the air-void system are initially measured by considering all voids without making any distinction with regards to shape. A second observation is also performed by separating the spherical air voids (the air bubbles) from the irregularly shaped compaction voids. Attempts were made to evaluate the capacity of the compaction air voids to remain unsaturated while the concrete was immersed in water. In the protection against frost, this ability is essential since unsaturated compaction air voids could act, at least in part, as air bubbles and consequently insure frost durability of dry concrete products. The pressure-saturation test, developed in Scandinavia, is a test procedure used to determine experimentally the volume of non-connected air voids in concrete. For the most part, this test procedure is quite similar to the ASTM C642 procedure. From various measurements, different characteristics of the concrete specimens can be determined such as the capillary absorption, the volume of capillary pores and the amount of non-connected air voids. With this test procedure, it is possible to evaluate if the masonry units contain a significant number of non-connected air voids that could act, at least in part, as air bubbles. The pressure-saturation tests were performed on samples from all mixtures (5 samples per mixture were tested). For the micro air-void analyses, samples from selected mixtures were observed using a scanning electron microscope. At a magnification of 700X, voids having a diameter of 1 µm can be easily observed. During the observations, the spherical voids were recorded. Table 3 summarizes the complete test program for Task 2. Information regarding the number of specimens tested is also given.
10
Table 3 - Testing Program
Set A Segmented retaining
wall units
Set B 4x8x16 masonry units
Date of production April 25th, 2002 April 18th, 2002
Number of mixtures produced (from different production runs)
16 15
Number of samples tested (per test per mixture)
Test carried out by NCMA R&D Laboratory
Compressive strength 5 5
Absorption 5 5
Density 5 5
F/T ASTM C1262
• In tap water 5 5
• In 3% saline solution 5 5 Test carried out by SEM
ASTM C457 2 2
Pressure saturation test 5 5
Micro air-voids 1 1
Task 2. Test Results Compressive Strength The compressive strength of all units was determined by NCMA Research & Development Laboratory in accordance with ASTM C140 Standard. As shown on the laboratory worksheets, units from Set A were not tested at the same age. Some units (16-A-ECA-NA, 13-A-Ref, 13-A-ECA-NA-FT, 13-A-ECA-NA, 10-A-ECA-NA-FT, 10-A-ECA-NA, 10-A-ECA-LA-FT, and 2xECA-HA-FT) were tested 54 days after casting. The remaining units of the Set A were tested after 84 days. On the other hand, some units from Set B (16-B-ECA-NA-FT, 16-B-ECA-NA, 16-B-ECA-LA-FT, 13-B-Ref, 13-B-ECA-NA, 10-B-ECA-NA-FT, and 10-B-ECA-NA) were
11
tested at 52 days and the other units were tested at 82 days. All results are given in Tables 4 and 5.
The compressive strength of units from Set A range from 3910 psi to 7090 psi. The compressive strength results of specimens from Set B are usually higher, ranging from 4860 psi and 9080 psi. All specimens developed good compressive strengths and have met the requirements for the minimum required compressive strength. With respect to ASTM C1372 Standard Specification for Segmental Retaining Wall Units, the average compressive strength of units shall be at least 3000 psi, with no individual result lower than 2500 psi. Absorption (ASTM C140 test procedure) The absorption and the density were also determined by NCMA Research & Development Laboratory on both sets of specimens in accordance with ASTM C140 Standard. As for the compressive strength tests, units from the same set were not tested at the same age. Units 16-A-ECA-NA, 13-A-Ref, 13-A-ECA-NA-FT, 13-A-ECA-NA, 10-A-ECA-NA-FT, 10-A-ECA-NA, 10-A-ECA-LA-FT, and 2xECA-HA-FT from Set A were tested at 12 days. All other units from Set A were tested one month later at the age of 42 days. Units 16-B-ECA-NA-FT, 16-B-ECA-NA, 16-B-ECA-LA-FT, 13-B-Ref, 13-B-ECA-NA, 10-B-ECA-NA-FT, and 10-B-ECA-NA for the Set B were tested at 45 days. The remaining units from Set B were tested at 75 days. The results are given in Table 4 and in Table 5. The average water absorption of units from Set A range from 4.4% to 7.2% (6.0 pcf to 9.7 pcf). Specimens from Set B have generally lower water absorption, results ranging from 3.0% to 5.3% (4.2 pcf to 7.2 pcf). With respect to ASTM C1372 Standard, the maximum water absorption requirements for normal weight concrete units should not be higher than 13 pcf. The density of units from Set A ranges from 133.1 pcf to 139.9 pcf. These results are similar to those obtained with units from Set B where the density varies from 136.4 pcf to 141.3 pcf (see Tables 4 and 5).
12
Table 4 - Compression Strength Test Results (SET A)
ID Compressive Strength Absorption Density
(psi) (MPa) (pcf) (%) (pcf) (kg/m3)
13-A-Ref 5870 40 9.7 7.2 134.0 2146
10-A-HW 5650 39 6.6 4.8 137.3 2199
13-A-HW 7090 49 6.9 4.9 139.7 2237
16-A-HW 6950 48 8.0 5.8 137.5 2202
10-A-ECA-NA 4310 30 7.6 5.7 133.1 2132
13-A-ECA-NA 5360 37 7.9 5.9 134.2 2149
16-A-ECA-NA 6100 42 7.8 5.8 135.6 2172
10-A-ECA-NA-FT 3910 27 8.8 6.6 132.8 2127
13-A-ECA-NA-FT 5800 40 7.3 5.3 136.7 2189
16-A-ECA-NA-FT 6050 42 7.5 5.6 135.6 2172
10-A-ECA-LA-FT 4570 32 6.0 4.5 133.6 2140
13-A-ECA-LA-FT 6860 47 6.1 4.4 139.7 2237
16-A-ECA-LA-FT 6570 45 6.6 4.8 136.7 2189
13-A-ECA-HA-FT 6590 45 6.9 4.9 139.9 2241
16-A-ECA-HA-FT 7010 48 7.5 5.4 138.0 2210
16-A-2xECA-NA-FT 6270 43 8.9 6.7 134.5 2154
13
Table 5 - Compression Strength Test Results (SET B)
ID Compressive Strength Absorption Density
(psi) (MPa) (pcf) (%) (pcf) (kg/m3)
13-B-Ref 6010 41 7.0 5.2 136.4 2184
10-B-HW 5800 40 5.4 4.0 137.0 2194
13-B-HW 8570 59 4.8 3.4 140.8 2255
16-B-HW 9080 63 4.6 3.2 141.2 2261
10-B-ECA-NA 5990 41 5.4 4.0 136.5 2186
13-B-ECA-NA 6890 48 4.7 3.4 139.0 2226
16-B-ECA-NA 7660 53 4.4 3.1 140.7 2253
10-B-ECA-NA-FT 4860 34 4.6 3.4 133.9 2144
13-B-ECA-NA-FT 7130 49 4.2 3.0 139.2 2229
16-B-ECA-NA-FT 7230 50 4.5 3.3 137.8 2207
10-B-ECA-LA-FT 5380 37 7.2 5.3 136.6 2188
13-B-ECA-LA-FT 6630 46 6.6 4.7 139.1 2228
16-B-ECA-LA-FT 8630 60 5.4 3.8 141.3 2263
13-B-ECA-HA-FT 7340 51 5.4 3.9 140.4 2249
16-B-ECA-HA-FT 7490 52 6.2 4.4 139.5 2234 Characteristics of the Air-void System The characteristics of the air-void system of all 31 mixtures were determined in accordance with a modified version of ASTM C457 Standard. As previously mentioned, all voids without making any distinction with regards to shape were first measured. The results are given in Tables 6 and 7. The irregularly-shaped compaction voids and the spherical air voids (the small air bubbles) were also recorded separately and results are also given in Tables 6 and 7.
14
Table 6 - Air-Void System Characteristics (SET A)
Standard ASTM C457 (All voids included)
Modified ASTM C457 (small compaction
voids only)
Modified ASTM C457
(small air bubbles
only) ID Total air
content, %
Specific area, mm-1
Spacing factor,
µm
Total air content,
%
Specific area, mm-1
Spacing factor,
µm
Total air content, %
13-A-Ref 18.1 11.3 103 14.4 13.7 111 0.0
10-A-HW 14.7 15.8 87 11.3 18.1 103 0.4
13-A-HW 10.2 13.0 141 6.9 13.5 205 0.7
16-A-HW 11.7 11.6 153 8.3 14.6 177 0.3
10-A-ECA-NA 18.7 10.5 100 13.2 13.8 114 0.1
13-A-ECA-NA 14.2 11.1 129 9.9 14.8 146 0.1
16-A-ECA-NA 13.1 13.9 136 10.6 16.1 150 0.1
10-A-ECA-NA-FT 26.0 5.9 115 12.4 11.5 145 0.0
13-A-ECA-NA-FT 14.0 13.1 117 10.9 14.7 140 0.1
16-A-ECA-NA-FT 12.8 10.9 172 8.9 13.8 205 0.2
10-A-ECA-LA-FT 16.5 15.6 65 9.1 17.6 113 1.2
13-A-ECA-LA-FT 8.8 20.0 122 6.2 15.3 232 1.2
16-A-ECA-LA-FT 10.9 16.9 129 8.4 14.6 201 1.1
13-A-ECA-HA-FT 10.0 14.9 128 8.1 14.3 167 0.2
16-A-ECA-HA-FT 17.6 8.8 111 15.0 8.0 146 0.6
16-A-2xECA-NA-FT 15.4 13.4 104 12.6 15.4 115 0.3
15
Table 7 - Air-Void System Characteristics (SET B)
Standard ASTM C457 (All voids included)
Modified ASTM C457 (small compaction
voids only)
Modified ASTM C457
(small air bubbles
only) ID Total air
content, %
Specific area, mm-1
Spacing factor,
µm
Total air content,
%
Specific area, mm-1
Spacing factor,
µm
Total air content,
%
13-B-Ref 15.3 13.3 77 12.6 15.2 84 0.1
10-B-HW 9.8 19.8 94 8.5 17.7 123 0.7
13-B-HW 8.3 16.8 134 6.7 13.6 210 1.0
16-B-HW 9.2 18.8 122 7.9 18.2 148 0.6
10-B-ECA-NA 12 14.4 103 10.4 15.2 115 0.1
13-B-ECA-NA 12.9 12.7 137 10.4 14.2 157 0.3
16-B-ECA-NA 22.4 8.1 122 9.8 16.9 134 0.3
10-B-ECA-NA-FT n/a n/a n/a n/a n/a n/a n/a
13-B-ECA-NA-FT 16.1 13.9 87 13.7 15.5 94 0.0
16-B-ECA-NA-FT 21 10.6 86 16.8 12.8 95 0.1
10-B-ECA-LA-FT 15.3 15.1 70 13.3 16.5 75 0.4
13-B-ECA-LA-FT 11 16.4 105 10 16.4 116 0.0
16-B-ECA-LA-FT 10.5 16.6 118 8.3 17.4 146 0.7
13-B-ECA-HA-FT 10.4 20.1 75 8.8 19.4 94 1.0
16-B-ECA-HA-FT 12.3 17.2 85 10.2 19.5 92 0.1
* not determined: too many compaction voids. Overall, the test results confirm that air entrainment in dry concrete mixtures is an extremely difficult task. In all cases, the small air bubble content (spherical voids content) is lower than 1.2%, and the high dosage air-entrained mixtures (13-A-ECA-HA-FT, 16-A-ECA-HA-FT and 13-B-ECA-HA-FT, 16-B-ECA-HA-FT) do not contain more spherical voids than the low dosage air-entrained mixtures or the non air-entrained mixtures. This indicates that most of the air voids probably result from the consolidation operations. Despite the low spherical air void contents measured, the test results show that the total air content was surprisingly high. The total air content is also very variable from one mixture to the other.
16
For instance, a total air content of 26% was measured for mixture 10-A-ECA-NA-FT (containing no air entraining admixture) while that of mixture 10-A-ECA-LA-FT is 16.5% (mixture containing the same percentage of cement with a low dosage of air entraining admixture). The difference is probably explained by the presence of compaction air voids. Results from Set A also indicate that the cement content influences the amount of voids in the concrete. In all cases, the mixtures containing 10% of cement (10-A-HW, 10-A-ECA-NA, 10-A-ECA-NA-FT, and 10-A-ECA-LA-FT) yielded the highest amount of small compaction voids when compared to companion concrete mixtures prepared with 13% (13-A-HW, 13-A-ECA-NA, 13-A-ECA-NA-FT and 13-A-ECA-LA-FT) or 16% of cement (16-A-HW, 16-A-ECA-NA, 16-A-ECA-NA-FT and 16-A-ECA-LA-FT). Furthermore, the results indicate that the use of higher dosage of water (high water mixtures such as 10-A-HW, 13-A-HW, 16-A-HW and 10-B-HW, 13-B-HW, 16-B-HW) clearly had a beneficial effect on the consolidation process. As shown in Table 6, the total air voids contents for these high water mixtures (for instance 10-A-HW) were lower than those of concrete mixtures prepared with lower water contents (10-A-ECA-NA, 10-A-ECA-NA-FT or 10-A-ECA-LA-FT) . The spacing factor is considered the most important parameter as regards to frost resistance. According to Pigeon and Pleau [8], the critical spacing factor concept can be used to evaluate the relative frost durability of various concretes. Although the critical spacing factor required to protect concrete against frost action depends on the type of concrete, the most widely recognized value is 230 µm. This is, for instance, the limit accepted by the Canadian standard A23.1 “Concrete Materials and Methods of Concrete Construction”. In Tables 6 and 7, the spacing factors are ranging from 65 µm to 172 µm (including air bubbles and small compaction voids). Due to the small air bubbles content (see Tables 6 and 7), the spacing factor results were not given since the formula can not take into account those small numbers without any major error. Pressure-Saturation Test The pressure-saturation test is used to determine experimentally the volume of non-connected air voids in concrete. Test results from this procedure usually yield very significant information. In normal concrete for instance, which contains a network of discontinuous spherical entrapped or entrained air voids, saturation of these voids does not generally occur under normal exposure conditions, even if the concrete has regular access to moisture. In dry concrete mixtures however, which contain a network of irregularly-shaped compaction air voids, some of these voids are connected and can thus become saturated if concrete has access to moisture. This, of course, may increase the damage due to frost. This testing procedure consists of oven-drying all specimens (20-mm thick discs with a 50-mm diameter) at 110° C until constant mass. The dry mass
17
(Md) of each disc specimen is measured, and all specimens are then immersed in a water bath kept at 20° C until constant mass. Each disc specimen remains immersed in water for a minimum period of 5 days. After this period, the saturated mass (Ms) of all specimens is measured. They are then placed in a pressure cell filled with water. The pressure of the cell is gradually raised to 15 MPa. Each disc is subjected to this pressure for a minimum period of 8 hours. This procedure is considered to be sufficient to completely fill with water all voids not previously saturated by capillary absorption. At the end of the pressure treatment, the pressure saturated mass (Mp) of each disc is determined. Finally, each disc is placed in a wire-steel cage immersed in water and tied to a scale by a wire. The mass of the immersed specimen (Mi) is then measured. From these various measurements, different characteristics of the concrete specimens are determined. Besides the capillary absorption (Ac), the volume of capillary pores (Vcp) and the amount of non-connected air voids (Vnc), which is assumed to be equal to the volume of voids saturated by the pressure treatment only, can be calculated by the following equations:
Ac (%) = Ms - MdMd
x 100 (1)
Vcp (%) = Ms - Md
Mi x 100 (2)
Vnc (%) = Mp - Ms
Mi x 100 (3)
The average results of the pressure saturation tests are summarized in Tables 8 and 9. The data include the volume of non-connected voids, the capillary absorption data, and the volume of capillary pores calculated for each mixture. As shown in Table 3, each individual result given in Tables 8 and 9 is the average result of 5 samples (50-mm disc).
18
Table 8 - Pressure-Saturation Test Results (SET A)
ID
Capillary absorption
(mass) %
Connected voids
(volume) %
Non-connected voids
(volume) %
13-A-Ref 6.8 14.5 4.5
10-A-HW 5.6 12.2 5.8
13-A-HW 5.3 12.0 4.3
16-A-HW 6.0 13.3 3.3
10-A-ECA-NA 8.0 16.8 3.5
13-A-ECA-NA 7.0 15.1 3.4
16-A-ECA-NA 5.8 12.6 5.2
10-A-ECA-NA-FT 6.0 13.0 6.3
13-A-ECA-NA-FT 6.1 13.2 4.3
16-A-ECA-NA-FT 5.5 12.2 3.9
10-A-ECA-LA-FT 5.5 11.8 6.5
13-A-ECA-LA-FT 5.7 12.6 5.1
16-A-ECA-LA-FT 5.7 12.4 5.4
13-A-ECA-HA-FT 5.1 11.4 4.9
16-A-ECA-HA-FT 5.5 12.2 4.2
16-A-2xECA-NA-FT 6.4 13.7 4.3
19
Table 9 - Pressure-Saturation Test Results (SET B)
ID
Capillary absorption
(mass) %
Connected voids
(volume) %
Non-connected voids
(volume) %
13-B-Ref 7.0 14.9 3.7
10-B-HW 5.1 11.0 5.8
13-B-HW 4.3 9.7 4.9
16-B-HW 5.4 11.8 4.7
10-B-ECA-NA 5.5 11.9 4.4
13-B-ECA-NA 5.5 11.9 4.2
16-B-ECA-NA 5.4 11.8 4.0
10-B-ECA-NA-FT 5.5 11.9 3.9
13-B-ECA-NA-FT 5.4 11.8 3.8
16-B-ECA-NA-FT 5.4 11.7 3.4
10-B-ECA-LA-FT 7.2 15.0 3.0
13-B-ECA-LA-FT 5.7 12.4 3.2
16-B-ECA-LA-FT 4.6 10.2 4.2
13-B-ECA-HA-FT 5.3 11.7 4.2
16-B-ECA-HA-FT 5.7 12.4 4.1
Despite the presence of numerous compaction voids, all mixtures from either Set A or Set B were found to have a capillary absorption of approximately 6%, individual results ranging from 5.3% to 8.0% for Set A and from 4.3% to 7.2% for Set B. It is generally believed that the water absorption of conventional good quality concretes should be in the 4 % to 5% range. The volume of non-connected voids is quite variable from one mixture to the other, results for Set A ranging from 3.3% to 6.5% and from 3.0% to 5.8% for mixtures from Set B, with no significant influence of the air entraining admixture. However, considering that a certain percentage of the total air voids are unconnected, the test results indicate that all mixtures contain a significant number of voids which could act, at least in part, as air bubbles.
20
Micro Air-Void Analyses by Scanning Electron Microscopy A total of 15 mixtures were selected and observed using a scanning electron microscope to see if the addition of an air-entraining admixture had contributed to entrain microscopic bubbles. The mixture selection was based on the characteristics of the air-void system and on the freeze-thaw durability test results (ASTM C1262) provided by NCMA. The selection was also based on the mixture composition and the different admixtures used. Most of the selected mixtures were from SET A (13-A-Ref, 10-A-HW, 13-A-HW, 16-A-HW, 13-A-ECA-NA, 13-A-ECA-NA-FT, 10-A-ECA-LA-FT, 13-ECA-LA-FT, 16-A-ECA-LA-FT and 13-ECA-HA-FT). Mixtures 13-B-Ref, 13-B-ECA-LA-FT and 13-B-ECA-HA-FT were also selected. For each selected mixture, a 25-mm disc (1 in.) was cut from a hardened concrete sample. The disc was dried at 40°C, and then impregnated with an epoxy resin. After a second impregnation, the disc was ground with a diamond paste over a rotating lead plate until the smoothness of the surface was good enough for the microscopical observations. For each disc, a surface of approximately 1 mm2 was examined. Observations were made at a magnification of 700X. At this magnification, voids having a diameter of 1 µm (0.00004 in.) can be easily observed. During the observations, only the spherical voids were recorded (irregularly-shaped voids were ignored). Complete results and analyses are presented in Table 10. Figures 1 to 5 present photographs from the SEM analyses. The microscopical observations usually provide significant information with regards to the size of the spherical air-voids. Air-entrained dry concrete mixtures often contain very small spherical air-voids. The diameter of these voids usually ranges from 10 µm (0.0004 in.) or less to 50 µm (0.002 in.). These very small air-voids can hardly be observed during the standard optical observations and are often present in dry concrete mixtures, particularly when high dosages of air-entraining admixtures are used.
21
Table 10 - Analyses by Scanning Electron Microscopy
Mixture ID SEM Analyses
13-A-Ref No small air bubbles were found. Compaction voids are mostly connected (see Figure 2).
10-A-HW No small air bubbles were found. Very high number of compaction voids are connected.
13-A-HW No small air bubbles were found. Voids were found in the aggregates.
16-A-HW No small air bubbles were found. 13-A-ECA-NA Only very small groups of air bubbles were found.
13-A-ECA-NA-FT A very small amount of air bubbles were found. Nothing significant. Connected compaction voids were observed at
a magnification of 20X. 10-A-ECA-LA-FT An average of 5 small air bubbles were found per mm2
(diameter lower than 10 µm) 13-A-ECA-LA-FT 19 small air bubbles with a diameter lower than 10 µm
were found per mm2 (see Figure 3). 16-A-ECA-LA-FT 17 small air bubbles (with a diameter lower than 10 µm)
were found per mm2 (see Figure 1). 13-A-ECA-HA-FT No small air bubbles were found. 16-A-ECA-HA-FT Very small air bubbles were found. 14 small air-bubbles
(under 10 µm in diameter) were found per mm2. 13-B-Ref No small air bubbles were found. 13-B-HW No small air bubbles were found.
13-B-ECA-LA-FT No small air bubbles were found (see Figure 4). 13-B-ECA-HA-FT No small air bubbles were found.
22
Figure 1 - Small air bubbles found in specimen 16-A-ECA-LA-FT (700X)
Figure 2 - General view of specimen 13-A-Ref (20X)
Air bubbles
Non hydrated cement particles
Cement paste
Compaction Voids
23
Figure 3 - Small air bubbles found in specimen 13-A-ECA-LA-FT (150X)
Figure 4 - Typical view for specimen 13-B-ECA-LA-FT (700X)
The scanning electron microscopy analyses clearly indicate that the majority of specimens have very little or no small air bubbles. The most microscopic spherical air
Partially hydrated Cement particles
Fine aggregates
Fine aggregates
24
voids recorded were for mixtures prepared with high dosage of an air entraining agent. Although few air bubbles were observed for specimens from mixtures 13-A-ECA-LA-FT, 16-A-ECA-HA-FT and 13-A-ECA-HA-FT, the amount of small air bubbles found is not significant enough to explain the volume of non-connected air-voids measured in the pressure saturation test. Voids were observed in some aggregates (see Figure 5). These voids could contribute to the total volume of non-connected voids in the mixture. Further investigation is needed in order to draw any conclusions and additional information will have to be provided by both producers (aggregate sources and properties).
Figure 5 - Typical voids found in the aggregates (140X)
Freeze-Thaw Durability (ASTM C1262) For the 31 mixtures, frost durability tests (in tap water and salt solution) were carried out by the NCMA Research & Development Laboratory in accordance with the requirements of ASTM C1262, a test method specifically designed to assess the frost resistance of masonry units. Five units are selected for freezing and thawing test and are placed in containers with water or saline solution for freezing-thawing cycles. A saturation period of 24 hours is required before submitting the samples to freeze-thaw cycles. During the freezing cycle, the air temperature in the chamber is maintained at –17 ± 5oC for a period of not less than 4 hours and not more than 5 hours. During the thawing cycle, the temperature in the chamber is maintained at 24o ± 5oC for a period not less than 2.5
Voids within aggregates
25
hours and not more than 96 hours. According to ASTM C 1372 Standard, specimens are considered durable to freezing-thawing cycles if they comply with either of the following situations: (1) weight loss of each of five test specimens at the conclusion of 100 cycles shall not exceed 1% of its initial weight; or (2) the weight loss of each of four of the five test specimens at the conclusion of 150 cycles shall not exceed 1.5% of its initial weight. A summary of the test results are presented in Tables 11 to 16. The ratio of the mass of residues collected at the end of testing to the initial mass of the specimen was calculated for each sample. Specimens tested in a saline solution were subjected to 40 freeze-thaw cycles while specimens tested in tap water were subjected to 100 freeze-thaw cycles. Each result is the average result of 5 specimens (individual results are given in brackets). A description of the failure mode was also provided by NCMA. Photographs showing the degradation of each set of specimens at the conclusion of the freeze-thaw testing are also available and presented in the Appendix. Extended testing was also carried out. Specimens were tested up to 100 cycles in a saline solution or 300 cycles in tap water unless significant mass loss was observed.
Figures 6 to 17 present the evolution of weight loss (%) during the ASTM C1262 test. Each curve is the average result of specimens exposed to freezing and thawing cycles during the entire duration of the test (only units that did not fail during testing were considered). For example, from Figure 8, results given for mixture 16-A-ECA-NA represent the average of four (4) samples even if measurements were performed on five (5) samples (one unit failed during testing). The figures presented hereafter contain the different results for each producer, each cement content and either exposure conditions (salt solution or tap water).
26
Table 11 - SET A Freeze-Thaw Results in Saline solution
Specimen Weight Loss (%) at 40 Cycles in Saline solution
ID Unit Weight Loss %
Failure mode (failed at cycles)
13-A-Ref 1.26
(1.12; 0.62; 0.97; 1.94; 1.66)
Top as laid only; Edges or corners; Locational; (80)
10-A-HW 0.41
(0.34; 0.34; 0.57; 0.37; 0.45)
All sides and edges; Edges or corners; (150)
13-A-HW 0.29
(0.22; 0.44; 0.34; 0.20; 0.24)
Both top and bottom only; All sides and edges; Edges or corners; Locational; (120)
16-A-HW 0.49
(0.45; 0.67; 0.68; 0.40; 0.23)
Bottom as laid only; Edges or corners; Locational; (80)
10-A-ECA-NA Terminated @ 25 cycles Edges or corners; Through the middle;
Locational; (25)
13-A-ECA-NA Terminated @ 25 cycles Edges or corners; Through the middle;
Locational; (25)
16-A-ECA-NA Terminated @ 30 cycles Edges or corners; Through the middle;
Locational; (30)
10-A-ECA-NA-FT Terminated @ 25 cycles Edges or corners; Through the middle;
Locational; (25)
13-A-ECA-NA-FT Terminated @ 30 cycles Top as laid only; Edges or corners;
Locational; (30)
16-A-ECA-NA-FT 8.46
(11.68; 9.36; 5.98; 4.40; 10.89)
All sides and edges; Edges or corners; Locational; (40)
10-A-ECA-LA-FT 1.28
(1.52; 1.42; 1.18; 1.31; 0.98)
All sides and edges; Edges or corners; (100)
13-A-ECA-LA-FT 1.32
(1.43; 1.28; 1.56; 1.30; 1.03)
Bottom as laid only; Edges or corners; (100)
16-A-ECA-LA-FT 0.78
(0.88; 0.47; 0.80; 0.80; 0.97)
Both top and bottom only; Edges or corners; (100)
13-A-ECA-HA-FT 0.65
(0.97; 0.42; 0.64; 0.54; 0.67)
Both top and bottom only; Edges or corners; Locational; (80)
16-A-ECA-HA-FT 2.55
(1.21; 0.87; 0.71; 9.36; 0.61)
Edges or corners; Through the middle; Locational; (70)
16-A-2xECA-NA-FT Terminated @ 30 cycles Edges or corners; Through the middle;
Locational; (30)
27
Table 12 - SET B Freeze-Thaw Results in Saline solution Specimen Weight Loss (%) at 40 Cycles in Saline solution
ID Unit Weight Loss %
Failure mode (number of cycles at failure)
13-B-Ref Terminated @ 35 cycles Edges or corners; Through the middle;
Locational; (35)
10-B-HW 0.98
(1.47; 1.02; 0.60; 0.86; 0.96)
All sides and edges; Edges or corners; Locational; (100)
13-B-HW 0.54
(1.00; 0.22; 0.37; 0.74; 0.35)
Both top and bottom only; Edges or corners; Locational; (100)
16-B-HW 0.34
(0.29; 0.20; 0.34; 0.46; 0.42)
Both top and bottom only; Edges or corners; Locational; (120)
10-B-ECA-NA Terminated @ 20 cycles Edges or corners; Through the middle;
Locational; (20)
13-B-ECA-NA Terminated @ 20 cycles Edges or corners; Through the middle;
Locational; (20)
16-B-ECA-NA Terminated @ 25 cycles Edges or corners; Through the middle;
Locational; (25)
10-B-ECA-NA-FT Terminated @ 20 cycles Edges or corners; Through the middle;
Locational; (20)
13-B-ECA-NA-FT 14.27
(19.87; 21.13; 2.57; 1.10; 26.70)
Edges or corners; Through the middle; Locational; (40)
16-B-ECA-NA-FT Terminated @ 30 cycles Edges or corners; Through the middle;
Locational; (30)
10-B-ECA-LA-FT 57.36
(17.74; terminated @ 30; 84.89; 82.08; 44.71)
Through the middle; Locational; Sudden complete disintegration; (40)
13-B-ECA-LA-FT 4.53
(6.38; 2.17; 7.93; terminated @ 30; 1.63)
Edges or corners; Through the middle; Locational; (40)
16-B-ECA-LA-FT 0.87
(1.13; 0.96; 0.32; 1.50; 0.44)
Both top and bottom only; Edges or corners; Locational; (100)
13-B-ECA-HA-FT 1.00
(1.35; 1.16; 0.86; 0.49; 1.13)
All sides and edges; Edges or corners; Locational; (80)
16-B-ECA-HA-FT 0.67
(1.32; 0.61; 0.55; 0.52; 0.34)
Edges or corners; Through the middle; Locational; (80)
28
Table 13 - SET A Freeze-Thaw Results in Tap Water Specimen Weight Loss (%) at 100 Cycles in Tap Water
ID Unit Weight Loss %
Failure mode (number of cycles at failure)
13-A-Ref 0.32
(0.21; 0.31; 0.29; 0.55; 0.25)
Bottom as laid only; All sides and edges; (300)
10-A-HW 0.12
(0.14; 0.08; 0.13; 0.11; 0.12)
Bottom as laid only; Edges only; (300)
13-A-HW 0.12
(0.10; 0.09; 0.17; 0.06; 0.17)
Bottom as laid only; Edges only; (300)
16-A-HW 0.20
(0.26; 0.23; 0.24; 0.09; 0.18)
Bottom as laid only; Edges only; (300)
10-A-ECA-NA 9.32
(23.27; 4.59; 3.75; 5.74; 9.27)
Bottom as laid only; Edges or corners; Locational; (100)
13-A-ECA-NA 1.50
(1.48; 1.29; 1.85; 2.22; 0.68)
Bottom as laid only; Edges or corners; Through the middle; (200)
16-A-ECA-NA 3.17
(3.99; 1.00; 1.58; 1.23; 8.07)
Bottom as laid only; Edges or corners; Locational; (125)
10-A-ECA-NA-FT 14.80
(2.99; 7.37; 5.90; 5.91; 51.83)
Bottom as laid only; Edges or corners; Locational; (100)
13-A-ECA-NA-FT 1.25
(1.00; 1.17; 0.95; 1.58; 1.55)
Bottom as laid only; Edges or corners; Locational; (300)
16-A-ECA-NA-FT 0.58
(0.59; 0.71; 0.56; 0.36; 0.67)
Bottom as laid only; All sides and edges; (300)
10-A-ECA-LA-FT 1.60
(2.30; 1.60; 1.80; 0.84; 1.48)
Bottom as laid only; All sides and edges; (300)
13-A-ECA-LA-FT 0.23
(0.22; 0.28; 0.19; 0.22; 0.26)
Bottom as laid only; (300)
16-A-ECA-LA-FT 0.49
(0.45; 0.29; 0.54; 0.56; 0.62)
Bottom as laid only; All sides and edges; (300)
13-A-ECA-HA-FT 0.47
(0.71; 0.39; 0.46; 0.29; 0.52)
Bottom as laid only; Edges only; (300)
16-A-ECA-HA-FT 0.28
(0.31; 0.19; 0.23; 0.36; 0.30)
Bottom as laid only; Edges only; (300)
16-A-2xECA-NA-FT 0.91
(1.35; 0.63; 0.84; 0.96; 0.79)
Bottom as laid only; Edges or corners; Locational; (150)
29
Table 14 - SET A Freeze-Thaw Results in Tap Water Specimen Weight Loss (%) at 100 Cycles in Tap Water
ID Unit Weight Loss after
100 cycles %
Unit Weight Loss after 150 cycles
%
Freeze-Thaw Durability (ASTM C 1372)1
13-A-Ref 0.32
(0.21; 0.31; 0.29; 0.55; 0.25) Not necessary Durable
10-A-HW 0.12
(0.14; 0.08; 0.13; 0.11; 0.12) Not necessary Durable
13-A-HW 0.12
(0.10; 0.09; 0.17; 0.06; 0.17) Not necessary Durable
16-A-HW 0.20
(0.26; 0.23; 0.24; 0.09; 0.18) Not necessary Durable
10-A-ECA-NA 9.32
(23.27; 4.59; 3.75; 5.74; 9.27)
Not necessary Not durable
13-A-ECA-NA 1.50
(1.48; 1.29; 1.85; 2.22; 0.68) Not necessary Not durable
16-A-ECA-NA 3.17
(3.99; 1.00; 1.58; 1.23; 8.07) Not necessary Not durable
10-A-ECA-NA-FT 14.80
(2.99; 7.37; 5.90; 5.91; 51.83)
Not necessary Not durable
13-A-ECA-NA-FT 1.25
(1.00; 1.17; 0.95; 1.58; 1.55) Not necessary Not durable
16-A-ECA-NA-FT 0.58
(0.59; 0.71; 0.56; 0.36; 0.67) Not necessary Durable
10-A-ECA-LA-FT 1.60
(2.30; 1.60; 1.80; 0.84; 1.48) Not necessary Not durable
13-A-ECA-LA-FT 0.23
(0.22; 0.28; 0.19; 0.22; 0.26) Not necessary Durable
16-A-ECA-LA-FT 0.49
(0.45; 0.29; 0.54; 0.56; 0.62) Not necessary Durable
13-A-ECA-HA-FT 0.47
(0.71; 0.39; 0.46; 0.29; 0.52) Not necessary Durable
16-A-ECA-HA-FT 0.28
(0.31; 0.19; 0.23; 0.36; 0.30) Not necessary Durable
16-A-2xECA-NA-FT 0.91
(1.35; 0.63; 0.84; 0.96; 0.79) 7.17
(4.01;1.87;14.95; 1.59; 13.45) Not durable 1 According to ASTM C 1372 Standard, specimens are considered durable to freezing-thawing cycles if they comply with either of the following situations: (1) weight loss of each of five test specimens at the conclusion of 100 cycles shall not exceed 1% of its initial weight; or (2) the weight loss of each of four of the five test specimens at the conclusion of 150 cycles shall not exceed 1.5% of its initial weight.
30
Table 15 - SET B Freeze-Thaw Results in tap Water Specimen Weight Loss (%) at 100 Cycles in tap Water
ID Unit Weight Loss %
Failure mode (number of cycles at failure)
13-B-Ref 0.81
(0.50; 0.91; 0.75; 0.66; 1.21)
Both top and bottom only; Edges or corners; Sudden complete
disintegration; (200)
10-B-HW 0.29
(0.28; 0.29; 0.19; 0.33; 0.34)
Bottom as laid only; Both top and bottom only; All sides and edges; (300)
13-B-HW 0.23
(0.29; 0.16; 0.30; 0.20; 0.21)
Both top and bottom only; All sides and edges; (300)
16-B-HW 0.43
(0.19; 0.33; 0.28; 1.01; 0.32)
Both top and bottom only; All sides and edges; Edges or corners; (300)
10-B-ECA-NA 1.73
(1.24; 1.50; 1.88; 1.56; 2.46)
Edges or corners; Through the middle; Locational; Sudden complete
disintegration; (150)
13-B-ECA-NA 3.90
(2.10; 3.20; 7.40; 2.80; 4.00)
Bottom as laid only; Through the middle; Locational; (100)
16-B-ECA-NA 0.61
(0.61; 0.65; 0.53; 0.70; 0.58)
Edges or corners; Through the middle; Locational; (200)
10-B-ECA-NA-FT 93.44
(100.0; 100.0; 100.0; 67.20; 100.0)
Sudden complete disintegration; (100)
13-B-ECA-NA-FT 1.43
(1.22; 1.28; 1.62; 1.32; 1.71)
Bottom as laid only; Edges or corners; Locational; (150)
16-B-ECA-NA-FT 1.72
(0.99; 1.29; 3.20; 1.34; 1.79)
Bottom as laid only; Edges or corners; Locational; (175)
10-B-ECA-LA-FT 1.16
(1.07; 0.87; 0.67; 1.57; 1.63)
Edges or corners; Through the middle; Locational; (225)
13-B-ECA-LA-FT 0.72
(0.71; 0.38; 0.52; 0.93; 1.06)
Top as laid only; Edges or corners; Locational; (300)
16-B-ECA-LA-FT 0.38
(0.36; 0.39; 0.38; 0.38; 0.39)
Bottom as laid only; Edges or corners; (300)
13-B-ECA-HA-FT 0.37
(0.50; 0.26; 0.24; 0.33; 0.52)
Top as laid only; Edges or corners; (300)
16-B-ECA-HA-FT 0.30
(0.34; 0.25; 0.33; 0.30; 0.29)
Top as laid only; Edges or corners; (300)
31
Table 16 - SET B Freeze-Thaw Results in Tap Water Specimen Weight Loss (%) at 100 Cycles in Tap Water
ID Unit Weight Loss after
100 cycles %
Unit Weight Loss after 150 cycles
%
Freeze-Thaw Durability (ASTM C
1372)1
13-B-Ref 0.81
(0.50; 0.91; 0.75; 0.66; 1.21) 1.33
(0.91; 1.51; 1.46; 0.90; 1.88) Not durable
10-B-HW 0.29
(0.28; 0.29; 0.19; 0.33; 0.34) Not necessary Durable
13-B-HW 0.23
(0.29; 0.16; 0.30; 0.20; 0.21) Not necessary Durable
16-B-HW 0.43
(0.19; 0.33; 0.28; 1.01; 0.32) 0.54
(0.26; 0.56; 0.42; 1.10; 0.38) Durable
10-B-ECA-NA 1.73
(1.24; 1.50; 1.88; 1.56; 2.46) Not necessary Not durable
13-B-ECA-NA 3.90
(2.10; 3.20; 7.40; 2.80; 4.00) Not necessary Not durable
16-B-ECA-NA 0.61
(0.61; 0.65; 0.53; 0.70; 0.58) Not necessary Durable
10-B-ECA-NA-FT 93.44
(100.0; 100.0; 100.0; 67.20; 100.0)
Not necessary Not durable
13-B-ECA-NA-FT 1.43
(1.22; 1.28; 1.62; 1.32; 1.71) Not necessary Not durable
16-B-ECA-NA-FT 1.72
(0.99; 1.29; 3.20; 1.34; 1.79) Not necessary Not durable
10-B-ECA-LA-FT 1.16
(1.07; 0.87; 0.67; 1.57; 1.63) Not necessary Not durable
13-B-ECA-LA-FT 0.72
(0.71; 0.38; 0.52; 0.93; 1.06) 1.10
(1.15; 0.56; 0.70; 1.36; 1.73) Durable
16-B-ECA-LA-FT 0.38
(0.36; 0.39; 0.38; 0.38; 0.39) Not necessary Durable
13-B-ECA-HA-FT 0.37
(0.50; 0.26; 0.24; 0.33; 0.52) Not necessary Durable
16-B-ECA-HA-FT 0.30
(0.34; 0.25; 0.33; 0.30; 0.29) Not necessary Durable 1 According to ASTM C 1372 Standard, specimens are considered durable to freezing-thawing cycles if they comply with either of the following situations: (1) weight loss of each of five test specimens at the conclusion of 100 cycles shall not exceed 1% of its initial weight; or (2) the weight loss of each of four of the five test specimens at the conclusion of 150 cycles shall not exceed 1.5% of its initial weight.
32
Table 17 – Comparison between Results of units from Set-A and Set-B (Specimen Weight Loss (%) in tap Water)
ID (SET-A) Freeze-Thaw
Durability (ASTM C 1372)
ID (SET-B) Freeze-Thaw
Durability (ASTM C 1372)
13-A-Ref Durable 13-B-Ref Not durable
10-A-HW Durable 10-B-HW Durable
13-A-HW Durable 13-B-HW Durable
16-A-HW Durable 16-B-HW Durable
10-A-ECA-NA Not durable 10-B-ECA-NA Not durable
13-A-ECA-NA Not durable 13-B-ECA-NA Not durable
16-A-ECA-NA Not durable 16-B-ECA-NA Durable
10-A-ECA-NA-FT Not durable 10-B-ECA-NA-FT Not durable
13-A-ECA-NA-FT Not durable 13-B-ECA-NA-FT Not durable
16-A-ECA-NA-FT Durable 16-B-ECA-NA-FT Not durable
10-A-ECA-LA-FT Not durable 10-B-ECA-LA-FT Not durable
13-A-ECA-LA-FT Durable 13-B-ECA-LA-FT Durable
16-A-ECA-LA-FT Durable 16-B-ECA-LA-FT Durable
13-A-ECA-HA-FT Durable 13-B-ECA-HA-FT Durable
16-A-ECA-HA-FT Durable 16-B-ECA-HA-FT Durable
16-A-2xECA-NA-FT Not durable
Results from Table 17 indicate that the freeze-thaw durability of most units produced by either producers are very similar for concretes having similar mixture compositions.
33
Table 18 – Comparison between Results of units from Set-A and Set-B (Specimen Weight Loss (%) in saline solution)
ID (SET-A) Freeze-Thaw Durability1 ID (SET-B) Freeze-Thaw
Durability1
13-A-Ref Not durable 13-B-Ref Not durable
10-A-HW Durable 10-B-HW Durable
13-A-HW Durable 13-B-HW Durable
16-A-HW Durable 16-B-HW Durable
10-A-ECA-NA Not durable 10-B-ECA-NA Not durable
13-A-ECA-NA Not durable 13-B-ECA-NA Not durable
16-A-ECA-NA Not durable 16-B-ECA-NA Not durable
10-A-ECA-NA-FT Not durable 10-B-ECA-NA-FT Not durable
13-A-ECA-NA-FT Not durable 13-B-ECA-NA-FT Not durable
16-A-ECA-NA-FT Not durable 16-B-ECA-NA-FT Not durable
10-A-ECA-LA-FT Not durable 10-B-ECA-LA-FT Not durable
13-A-ECA-LA-FT Not durable 13-B-ECA-LA-FT Not durable
16-A-ECA-LA-FT Durable 16-B-ECA-LA-FT Durable
13-A-ECA-HA-FT Durable 13-B-ECA-HA-FT Durable
16-A-ECA-HA-FT Not durable 16-B-ECA-HA-FT Durable
16-A-2xECA-NA-FT Not durable 1 Specimens are considered durable against freezing-thawing cycles in saline solution if the average value of the weight loss (and not the weight loss of each of five test specimens) is < 1%. From Table 18, the results show that units cast by both producers, with similar mixture compositions, behave similarly when exposed to freezing and thawing cycles in saline solution (with the exception of the mixture made with 16% cement, ECA and HA).
34
Table 19 – Comparison between Durability results of units from Set-A in tap water and in saline solution
ID (SET-A) Freeze-Thaw Durability in tap water
Freeze-Thaw Durability in saline solution
13-A-Ref Durable Not durable
10-A-HW Durable Durable
13-A-HW Durable Durable
16-A-HW Durable Durable
10-A-ECA-NA Not durable Not durable
13-A-ECA-NA Not durable Not durable
16-A-ECA-NA Not durable Not durable
10-A-ECA-NA-FT Not durable Not durable
13-A-ECA-NA-FT Not durable Not durable
16-A-ECA-NA-FT Durable Not durable
10-A-ECA-LA-FT Not durable Not durable
13-A-ECA-LA-FT Durable Not durable
16-A-ECA-LA-FT Durable Durable
13-A-ECA-HA-FT Durable Durable
16-A-ECA-HA-FT Durable Not durable
16-A-2xECA-NA-FT Not durable Not durable
35
Table 20 – Comparison between Durability results of units from Set-B in tap water and in saline solution
ID (SET-B) Freeze-Thaw Durability in tap water
Freeze-Thaw Durability in saline solution
13-B-Ref Not durable Not durable
10-B-HW Durable Durable
13-B-HW Durable Durable
16-B-HW Durable Durable
10-B-ECA-NA Not durable Not durable
13-B-ECA-NA Not durable Not durable
16-B-ECA-NA Durable Not durable
10-B-ECA-NA-FT Not durable Not durable
13-B-ECA-NA-FT Not durable Not durable
16-B-ECA-NA-FT Not durable Not durable
10-B-ECA-LA-FT Not durable Not durable
13-B-ECA-LA-FT Durable Not durable
16-B-ECA-LA-FT Durable Durable
13-B-ECA-HA-FT Durable Durable
16-B-ECA-HA-FT Durable Durable
Results from Tables 19 and 20 clearly indicate that freezing-thawing durability tests carried out in a saline solution are more severe than in tap water. Some units pass the test in tap water but not in saline solution. Nevertheless, all units that failed the test after 100 cycles in tap water also failed the test after 40 cycles in saline solution. As explained by Pigeon et Pleau [8], in presence of salts, the formation of ice is delayed and large hydraulic pressures are generated causing much more damage (weight loss in this case).
Figure 6 - ASTM C1262 - SET A - Tap Water 10% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
10-A-HW
10-A-ECA-NA
10-A-ECA-NA-FT
10-A-ECA-LA-FT
Figure 7 - ASTM C1262 - SET A - Tap Water 13% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
13-A-HW
13-A-ECA-NA
13-A-ECA-NA-FT
13-A-ECA-LA-FT
13-A-ECA-HA-FT
Figure 8- ASTM C1262 - SET A- Tap Water 16% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
16-A-HW
16-A-ECA-NA
16-A-ECA-NA-FT
16-A-ECA-LA-FT
16-A-ECA-HA-FT
16-A-ECA2x-NA-FT
Figure 9 - ASTM C1262 - SET A - Saline Solution 10% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
10-A-HW
10-A-ECA-NA
10-A-ECA-NA-FT
10-A-ECA-LA-FT
Figure 10 - ASTM C1262 - SET A - Saline Solution 13% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
13-A-HW
13-A-ECA-NA
13-A-ECA-NA-FT
13-A-ECA-LA-FT
13-A-ECA-HA-FT
Figure 11 - ASTM C1262 - SET A - Saline Solution 16% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-A-Ref
16-A-HW
16-A-ECA-NA
16-A-ECA-NA-FT
16-A-ECA-LA-FT
16-A-ECA-HA-FT
16-A-ECA2x-NA-FT
Figure 12 - ASTM C1262 - SET B - Tap Water 10% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref
10-B-HW
10-B-ECA-NA
10-B-ECA-NA-FT
10-B-ECA-LA-FT
Figure 13 - ASTM C1262 - SET B - Tap Water 13% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref
13-B-HW
13-B-ECA-NA
13-B-ECA-NA-FT
13-B-ECA-LA-FT
13-B-ECA-HA-FT
Figure 14 - ASTM C1262 - SET B - Tap Water 16% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 50 100 150 200 250 300
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref
16-B-HW
16-B-ECA-NA
16-B-ECA-NA-FT
16-B-ECA-LA-FT
16-B-ECA-HA-FT
Figure 15 - ASTM C1262 - SET B - Saline Solution 10% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref
10-B-HW
10-B-ECA-NA
10-B-ECA-NA-FT
10-B-ECA-LA-FT
Figure 16 - ASTM C1262 - SET B - Saline Solution 13% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref
13-B-HW
13-B-ECA-NA
13-B-ECA-NA-FT
13-B-ECA-LA-FT
13-B-ECA-HA-FT
Figure 17 - ASTM C1262 - SET B - Saline Solution
16% of Cement
0,0
1,0
2,0
3,0
4,0
5,0
0 25 50 75 100 125 150
Number of Freeze-Thaw Cycles
Wei
ght L
oss
(%)
13-B-Ref16-B-HW16-B-ECA-NA16-B-ECA-NA-FT16-B-ECA-LA-FT16-B-ECA-HA-FT
48
As specified in ASTM C1372 Standard, a weight loss of 1% after 100 freezing and thawing cycles in tap water is usually considered as the maximum limit for performance. Current industry recommendations for units used in severe weathering areas with exposure to saturation and de-icing salts is 40 cycles in saline solution. However, is a 100 freezing and thawing cycles in tap water is equivalent to 40 cycles in a saline solution? Results given in Tables 19 and 20 are in good agreement with these recommendations with some exceptions (16-B-ECA-NA tested in tap water was 0.61% after 100 cycles while specimens from the same mixture exceeded 5% weight loss after 25 cycles when tested in a saline solution or 16-A-ECA-NA-FT : a weight loss of 8.46% at the conclusion of the 40 freezing and thawing cycles in a saline solution while, in tap water, the weight loss of the specimens was limited to 0.58% at 100 cycles). The results clearly show the detrimental effect of the saline solution. For the same number of freeze-thaw cycles, the deterioration is always higher for samples exposed to a saline solution. The use of an air entraining admixture was found to have a beneficial effect on the frost durability of the concrete mixtures tested. This is particularly true for high dosage mixtures. With the exception of one (mixture 16-A-ECA-HA-FT), all mixtures containing a high dosage of air entraining admixture (mixtures 16-A-ECA-LA-FT, 13-A-ECA-HA-FT, 16-B-ECA-LA-FT, 13-B-ECA-HA-FT and 16-B-ECA-HA-FT) met the requirements of ASTM C1372 Standard. When tested in a saline solution, results range from 0.65% to 1.00%, and from 0.28 % to 0.47% when tested in tap water. The average weight loss of mixture 16-A-ECA-HA-FT after 40 cycles of freezing and thawing in a saline solution was 2.55 % (the marginal (and high) value of 9.36% was considered in the calculation). Results from Tables 11 to 16 do not indicate that the use of the FT Enhancing admixture markedly influenced the frost durability of the specimens. As shown by the results obtained for mixtures 10-A-ECA-NA-FT, 13-A-ECA-NA-FT, 16-A-ECA-NA-FT, 10-B-ECA-NA-FT, 13-B-ECA-NA-FT and 16-B-ECA-NA-FT, the sole use of this admixture, without the combined use of an air entraining admixture, is not sufficient to insure good frost durability. With the exception of mixture 16-A-ECA-NA-FT which performed well when tested in tap water, all mixtures prepared with a FT Enhancing admixture had a weight loss higher than 1% at the conclusion of testing. The test results also indicated that the addition of an Efflorescence Control Admixture (ECA) without any other admixture was detrimental to the frost durability of the concrete mixtures. When tested in a saline solution, mixtures 10-A-ECA-NA, 13-A-ECA-NA, 16-A-ECA-NA, 10-B-ECA-NA, 13-B-ECA-NA and 16-B-ECA-NA performed poorly and tests were stopped before completion of all the cycles recommended. With the exception of mixture 16-B-ECA-NA prepared with 16% of cement (weight loss of 0.61% after 100 cycles), all mixtures containing the ECA admixture did not meet the requirements of the standard when specimens were subjected to freezing and thawing cycles in tap water. As shown in Tables 11 to 16, mixtures prepared with a higher water content have systematically performed better to the freezing and thawing cycles (in both tap water and in a saline solution) than the other mixtures. This is probably due, at least in part, to a
49
better consolidation process caused by a better dispersion of the cement grain in the concrete thus improving the homogeneity of the paste. The extended testing (150 cycles in a saline solution and 300 cycles in tap water solution) has shown that the concrete weight loss is often higher than 1% after a large number of cycles. These results probably indicate that the deterioration of the concrete specimens is not a surface related issue but is more related to the composition of the mixture. It is important to mention that some mixtures have performed very well under 300 cycles in a tap water solution (i.e., 10-A-HW, 13-A-HW, 16-A-HW, 13-A-ECA-LA-FT and 16-A-ECA-HA-FT) which clearly show that these types of concretes can be frost resistant. Task 2 Discussion of test results Air Entrainment As previously mentioned, air entrainment in dry concrete mixtures is a very difficult task. The results obtained clearly confirm that air entrainment in dry concretes is much more complex than in conventional concretes. The absence of air bubbles in most of the mixtures is not surprising (even those with an air entraining admixture). Similar results were reported in the literature [2 - 6]. The air voids measurements (see Tables 6 and 7) indicate that some mixtures contain a very high amount of compaction voids (i.e., mixtures 10-A-ECA-NA-FT and 10-B-ECA-NA-FT) and no spherical air bubbles. Although some compaction voids can be very efficient in protecting the concrete against the freeze-thaw action, they can have a detrimental effect if they are interconnected. When connected, water can easily be absorbed by the concrete before freezing. The absorption tests confirmed that some mixtures absorb water quickly and become rapidly saturated. The pressure saturation test showed that most dry concrete specimens tested have an important amount of disconnected air voids (3% to 4%). So far, this could not be fully explained by the presence of small compaction voids and small air bubbles. From the scanning electron microscope observations and analyses, the air entraining admixture used in this Study did not allow the formation of a significant amount of very small air bubbles. Only a very few amount of small air bubbles were found in some mixtures. Can these few microscopic air bubbles have somewhat a beneficial influence on the frost resistance of these concretes ? Although some specimens had a very small amount of air bubbles (i.e. 13-A-ECA-LA-FT and 16-A-ECA-HA-FT), others prepared with an air entraining admixture had close to none. Why most concrete mixtures in this Study do not appear to have any spherical air bubbles? This is probably due, at least in part, to the mixing process and the very low
50
water content. Air entrainment requires a mechanical action to draw air into the mixture [7]. The two basic processes are kneading, in which air is entrapped as concrete falls on itself, and stirring, in which a vortex created draws air into the mixture. Air entrainment also requires a sufficient amount of time. In this case, the mechanical action and the length of time were probably not sufficient to produce the required number of spherical air bubbles. Furthermore, to entrain air, a sufficient amount of water is needed in order to form a film around all bubbles [7]. The amount of water in most mixtures tested was probably too low to wet all the aggregates and the cement particles and, at the same time, allow the formation of air bubbles. As previously mentioned in the Task 1 Report, air voids offer a good protection against frost action when they are spherical in shape and fairly well dispersed in the cement paste. Although 230 µm is often considered the critical spacing factor below which concrete mixtures are considered to be properly protected against frost action, this value should be considered with caution [2]. The fact that the air void system of most mixtures was found to mostly consist of compaction voids underlines the somewhat ambiguous application of the ASTM C457 Standard to dry concrete products. It is clear that the characterization of an air void system made of large irregularly-shaped compaction voids using a method designed for spherical air voids of various sizes is not appropriate. This is especially true considering that, in the literature, the role played by these voids in the protection against frost damage has never been clearly established. SEM observations also showed that most of the compaction voids were interconnected: if so, when they become saturated (if enough water is available), their influence on the frost resistance may be detrimental. Figure 18 shows the relationship between the small air voids content of units from Set A and Set B and their total air voids content (provided by the ASTM C457 Standard measurement). This Figure indicates that, in most cases, the content of small compaction voids (voids < 75 µm) increases with the total air voids content (all Voids included). This is quite interesting considering that a higher content of small compaction voids that could provide a good protection against frost action would inevitably yield to an increase in large compaction voids usually considered detrimental to the freeze/thaw durability since they are generally interconnected.
51
0
5
10
15
20
25
30
0 5 10 15 20 25 30
All Voids Included %
Sm
all c
ompa
ctio
n vo
ids
(< 7
5 µm
), %
Figure 18 – Relation between total air voids content versus small air voids content Freeze-Thaw durability The ASTM C1262 tests were carried out in tap water and in a saline solution. From the test results, the presence of salt in the solution during the freeze-thaw testing was found to be mostly detrimental (deterioration occurs rapidly) to the frost resistance of these concrete mixtures. This can probably be explained by the rapid freezing of the saline solution (which is faster than tap water) caused by supercooling [8]. The types of concretes addressed in this study have usually low water-cement ratios compared to conventional concrete which result in a low paste porosity. When concrete is properly compacted and made with good quality aggregates, it can be considered that three basic parameters determine the resistance to freezing and thawing: the paste porosity (i.e total porosity and pore size distribution), the air void spacing factor, and the characteristics of the freezing and thawing cycle. In this Study,
52
the consolidation process greatly influenced the frost resistance of the concrete. Mixtures prepared with a higher water content were found to have a good frost resistance and met the requirements of the standard even when tested in a saline solution. Furthermore, mixtures 10-A-HW, 13-A-HW and 16-A-HW even performed well when subjected to 300 cycles in tap water. In this case, a higher water content probably increased the homogeneity of the paste and allowed a better consolidation of the concrete with fewer connected compaction voids. Globally, test results indicate that the use of an air entraining admixture influenced the frost resistance of the concrete mixtures. Some air-entrained mixtures, particularly those with a high dosage of air entraining admixture, met the requirements of the standard in both tap water and in a saline solution. Scanning electron microscope observations also confirmed that microscopic air bubbles were detected when high dosage of air entraining admixture was used (mixtures 13-A-ECA-LA-FT, 16-A-ECA-LA-FT and 16-A-ECA-HA-FT). The fact that it was possible to entrain a certain amount of small spherical air bubbles in a few mixtures tends to indicate that, if difficult, air entrainment in dry concrete mixtures is not totally impossible. Furthermore, the use of an air entraining admixture usually increases slightly the paste volume, reduces the viscosity of the paste and therefore enhances the workability of the concrete. By increasing the workability, the consolidation process may have been improved. Based on the test results, the addition of an efflorescence-control admixture (ECA) was also found to be detrimental to the frost durability of the concrete mixtures. This may be explained, at least partly, by the fact that water movements are impeded in concrete mixtures containing this type of admixture. If water freezes too rapidly and does not have time to go into empty air bubbles or compaction voids, internal pressures are built inside the concrete. These pressures can damage the material and lead to its failure. Finally, coarse aggregates can influence the frost durability of concretes [8]. As previously mentioned, the properties of the aggregates were not made available in this Study. However, as shown in Figure 20, porous aggregates were observed. At this stage, it is not clear if this can explain, at least partly, the amount of disconnected compaction voids obtained.
53
Porous aggregates
Figure 20 - Porous aggregates (700X)
Concluding Remarks All mixtures tested in this Study met the requirements of the ASTM C 1372 Standard with regards to the minimum compressive strength and the maximum acceptable water absorption. However, most mixtures were found to be susceptible to frost damage (approximately 60% of the mixtures tested in tap water and 66% of the mixtures tested in a saline solution were found to be susceptible to frost action). This clearly indicates that other parameters must be considered to produce frost resistant dry concrete mixtures. In conventional concrete, the frost resistance, like almost all properties of concrete, is closely related to the water/cement ratio. This valuable parameter could not be determined for all mixtures tested since the total water content was not known to the investigators. More complete analyses could have been carried out if, notwithstanding the water/cement ratio, other relevant data, such as the properties of the aggregates, had been made available. From results obtained in this study, we can however conclude that more important the water content in the mixture, better is the performance of concrete units to frost durability tests. The necessity of entraining air bubbles in concrete masonry units for adequate frost protection remains an open question: good frost durability appears to be possible without the use of air entraining admixtures. Although the stiff consistency of these mixtures generally makes air entrainment very difficult, results indicate that it is possible
54
in certain cases to entrain small spherical air bubbles. It however involves a more complicated procedure than simply adding an air entraining admixture to the concrete in the mixer. The usefulness and the efficiency of air entraining admixtures is still ambiguous. The type of air entraining admixtures (and their dosage) and the type of mixer could play an important role. Other than the air entraining admixture, different admixtures were used in some concrete mixtures. Although the FT enhancing admixture had little influence on the frost durability of the concrete mixtures, the efflorescence control admixture (ECA) was found to have a detrimental effect on the behavior of the concrete mixtures when exposed to repeated cycles of freezing and thawing in either a saline solution or in tap water. This admixture probably affects the movement of water into the concrete, creating internal pressures that may cause damage to the concrete when exposed to frost action. The Study also clearly emphasized the importance of the consolidation process on the internal structure of the concrete mixtures which influences the frost durability. The role played by the non-connected small compaction voids is still unclear. Variations in the mixture compositions as well as the curing method were not investigated and could have influenced the frost durability of the mixtures tested. Many questions remain unanswered. What are the conditions under which the internal structure of concrete masonry units vary ? How can the homogeneity of the paste be improved and thus perhaps increase the frost durability ? Is there a minimum cement or paste content required to obtain a sufficiently compact and durable internal structure ? The "indicators" for the production of frost resistant concrete masonry units will be identified in the next Task which will lead to the elaboration of the "Frost durability Index".
55
References [1] Memorandum (1998), Preliminary Results of NCMA Phase 3 Durability Research,
Publication of the NCMA Durability Task Group, 23 p. [2] Marchand, J., Boisvert, L., Tremblay, S., Maltais, J., Pigeon, M. (1998), Air
Entrainment in Dry Concrete Mixtures, Concrete International, Vol. 20, No 4, April, pp. 38-44.
[3] Marchand, J.,Pigeon, M., Boisvert, J., Isabelle, H. L., and Houdusse, O. (1992),
Deicer Salt Scaling Resistance of Roller-Compacted Concrete Pavements Containing Fly Ash and Silica Fume, American Concrete International, SP-132, Detroit, pp. 151-178.
[4] Boisvert, J., Marchand, J., Pigeon, M., and Isabelle, H. L. (1992), Freeze-Thaw
Durability and Deicer Salt Scaling Resistance of Concrete Paving Blocks, Canadian Journal of Civil Engineering, Vol. 19, No 6, pp. 1017-1024. (in French)
[5] Marchand, J., Boisvert, J., Pigeon, M., and Isabelle, H. L. (1991), Deicer Salt Scaling
Resistance of Roller-Compacted Concrete Pavements, American Concrete Institute, SP-126, Detroit, pp. 131-153.
[6] Marchand, J., Pigeon, M., Isabelle, H. L., and Boisvert, J. (1990), Freeze-Thaw
Durability and Deicer Salt Scaling Resistance of Roller-Compacted Concrete Pavements, American Concrete Institute, SP-122, pp. 217-236.
[7] Powers, T.C. (1964), Topics in Concrete Technology: Part 3 - Mixes Containing
Intentionally Entrained Air, Journal of PCA Research and Development Laboratories, V 6, No. 3, pp. 19-42.
[8] Pigeon, M. and Pleau, R. (1995), Durability of Concrete in Cold Climates, E & FN
SPON, 244p.
STUDY ON IMPROVING THE FROST DURABILITY OF CONCRETE MASONRY PRODUCTS
Final Report Task 3 – Frost Durability Index
Project SEM00110
Presented to:
Mr. Robert D. Thomas National Concrete Masonry Association
2302 Horse Pen Road Herndon, VA 20171-3499
USA
May 2004
2
THE STUDY - OBJECTIVES AND PROJECT DESCRIPTION The main objective of the study was to develop an index that could be used to predict the frost durability of masonry units. This frost durability index would be based on a determination of the total volume of disconnected (unsaturated) voids, and could be used by producers to improve the durability of their products. The elaboration of this index could also help to establish acceptable limits for the degradation of masonry units tested in tap water and salt solution. The study is divided into the following six tasks: • Task 1 - Literature Survey • Task 2 - Data Analysis and Additional Testing • Task 3 - Elaboration of a Frost Durability Index • Task 4 - Laboratory Validation of the Durability Index • Task 5 - Assessment of the Frost Durability of Existing Masonry Units • Task 6 - Reporting. Each of the first five tasks addresses a specific aspect of the problem. The last task consists of the preparation of the progress and final reports. The project was elaborated in such a way as to optimize the use of existing data on the frost durability of masonry units generated by the National Concrete Masonry Association (NCMA). The first part of the study, a comprehensive search of the scientific and technical literature, was conducted in 2001. The final report for Task 1 was submitted to the NCMA on August 7, 2001. The second part of the study, data analysis and additional testing report, was conducted in 2003. The final report for Task 2 was submitted to the NCMA on January 31, 2004. Task 2. Background Information The present study was to be based mainly on a series of readily available parameters provided by a previous study conducted by the NCMA in 1998-1999 [1]. In that research, a total of 30 sets of concrete masonry units were manufactured and tested to determine the effect of various production parameters on their freeze-thaw durability. Test variables included type of cement (Type I and Type III), mixture water content, use of various chemical admixtures (water repellent, plasticizer), aggregate gradation and compaction time. The compressive strength, absorption and density of all mixtures were determined.
3
Frost durability tests were also carried out (in tap water and salt solution) according to the requirements of ASTM C1262. In 2001, the NCMA decided that the current study would not be based on the results provided by the 1998-1999 study, but rather on test results from an entirely new set of units. A total of 31 different mixtures were manufactured in April 2002 by two different producers. Many test variables were also modified. All the changes made to the original research program as well as the information regarding the production of these new units are provided in a subsequent section of the progress report (see New Mixture Composition). The compressive strength, absorption and density of all 31 concrete mixtures were determined by the NCMA Research & Development Laboratory. Frost durability tests were also carried out by the NCMA (in tap water and salt solution) according to the requirements of ASTM C1262. The results were made available in January 2003. Pressure Saturation Tests were conducted by SEM in their laboratory to determine the volume of non-connected voids. Task 2. New Mixture Composition As previously mentioned, a total of 31 different mixtures were produced by two different producers (Set A and Set B). Set A, provided from 16 different production runs, consisted of specimens from Versa-Lok retaining wall units produced by Barnes and Cone of Syracuse (New York). Set B was taken from 4x8x16 concrete masonry units made at the World Concrete Center in Alpena, Michigan from 15 different production runs. The new test variables are the following: - mixture water content - cement content - aggregate gradation - use and dosage of various chemical admixtures (air-entraining, FT enhancer, calcium
stearate dispersing admixtures). A Type I cement was used for both sets of specimens. The cement was supplied by two different manufacturers. The use of a Type III cement is no longer a variable in the study. Furthermore, the compaction time of all mixtures was not monitored during production. A code identification (ID) was developed to identify each mixture with respect to its composition and origin. The first number of the mixture codification indicates the quantity
4
of cement used in the mixture (%). The letter that follows identifies the producer (Producer A or Producer B). In this report, it is also referred to as Set A (Producer A) and Set B (Producer B). The remaining letters are all related to the constituent. The following are the definitions for all the abbreviations used:
• HW stands for high water content • ECA indicates the use of an efflorescence control admixture • NA indicates that no air-entraining admixture was used • LA means that a low dosage of air-entraining admixture was used • HA means that a high dosage of air-entraining admixture was used • FT indicates the use of a freeze-thaw enhancer admixture • Ref refers to the reference mixture (no admixture).
For instance, 16-A-ECA-LA-FT means that the mixture contains 16% cement (mass). It was produced by Producer A. The mixture also contains an efflorescence control admixture (ECA), an air-entraining admixture at a low dosage (LA) and a quantity of a freeze-thaw enhancer admixture (FT). ECA stands for calcium stearate dispersing admixture, which has been used in the industry as the raw material for most integral water repellent admixtures. FT enhancer is an admixture marketed as an improver of freeze-thaw durability (it is not an air-entraining admixture). Task 3. Frost Durability Index This part of the study consists of the determination of a frost durability index based on the experimental data generated in Task 2. First, traditional mathematical tools were used to determine the relationship between mass loss and the different parameters studied (compressive strength, cement content, absorption, density, water content, etc.) However, no linear relationship exists between the different parameters and the frost durability results. Therefore, a more sophisticated method capable of finding a non-linear relationship between the different parameters or combinations of parameters and the freeze-thaw results was used. This method is called the Neural Network Method, which will be explained in more detail in the next section.
5
1. General Principles of the Neural Network A neural network is composed of simple elements operating in parallel. As in nature, the network function is largely determined by the connections between elements. A neural network can be trained to perform a particular function by adjusting the values of the connections (weights) between elements. In short, neural networks are trained in such a way that particular inputs lead to a particular target output. As shown in Figure 1, batch training of a neural network proceeds by effecting weight and bias changes based on an entire batch of input data.
Figure 1 –Neural network architecture To analyze the freeze-thaw results in tap water and saline solution, multi-layered networks were used with respect to two functions: a non-linear sigmoid function between the input data and the first layer of hidden neurons, and a linear function between the hidden neurons and the output neuron (see Figure 2). 2. Procedure for Establishing the Neural Network It was impossible to use traditional mathematical methods to determine the parameters that have the most weight on the freeze-thaw durability results for dry segmental retaining wall tested in tap water. The neural network method was therefore used to identify the most reliable parameters and combinations of parameters for the durability results. Sixteen parameters were retained. These parameters are presented in Table 1.
Neural Network, including connections
(weights) between neurons
Input Output Compare: Error
function
Target
Adjust weights
6
The same procedure was adopted for the experimental results of the testing conducted in tap water and saline solution. The adopted procedure comprised the six following steps: 1. A neural network is built and trained with the input data (values of the various
parameters presented in Table 1 and the mass loss results expressed in % for 24 samples).
2. For each input parameter, the network predicts the durability results for the 6 test
samples (these samples are in bold in Table 2). 3. The numerical results are compared with the experimental results obtained for the 6
samples. An error is calculated following each simulation. 4. The parameter that produces the minimal error is retained. 5. Following that, another simulation is conducted to find the combination of the
parameter retained in the previous step and the other parameters in the series of remaining parameters so as to produce an error lower than that obtained in step 4.
6. Several parameters are combined successively and the error induced at each
combination is calculated until it is equal to or higher than the error obtained in step 5.
7. Once the more reliable parameters have been retained, the hidden neuron number is
increased, with the number varying from 2 to 10. An error is calculated for each possibility and the hidden neuron number yielding the minimal error is retained.
7
Table 1 – Parameters used in the neural network and their respective units
Parameters tested Unit
1 - Compressive strength psi 2 - Absorption pcf 3 - Density pcf 4 - Total air content, considering all voids % 5 - Spacing factor, considering all voids µm 6 - Total air content, considering small compaction voids only % 7 - Spacing factor, considering small compaction voids only µm 8 - Total air content, considering small air bubbles only % 9 - Cement content % 10 - Capillary absorption % 11 - Connected voids % 12 - Non-connected voids % 13 - Use of ECA 1 if used or 0 if not 14 - Use of high water content 1 if used or 0 if not 15 - Use of FT enhancer 1 if used or 0 if not 16 - Use of no, low or high dosage of air-entraining admixture 0 if no air, 1 if low air, 2 if high air
8
Figure 2 – Neural network used in this study 3. Neural Network for Freeze-Thaw Test in Tap Water To determine the frost durability index for specimens tested in tap water, 16 parameters were used as input data (see Table 1). The output data represents the mass loss (%) in tap water after 100 cycles. As explained in Table 2 and Figure 2, our neural network was built and trained with 24 results corresponding to 24 samples (concrete mixtures), and 6 samples (concrete mixtures) were kept aside to determine the precision of (or test) our neural network.
PR
P2
P1
Input Data
Hidden Neurons
Output Data
W11
W21
WRn
b1
b2
bn-1
bn
First layer, output is calculated by sigmoid function
Second layer, output is calculated by linear function
9
The specimens used to train the network (24 samples) and the specimens used to test the retained neural network (6 samples in bold) are presented in Table 2.
Table 2 – Specimens tested in tap water and used for network training and validation
ID (SET-A) 1 Freeze-Thaw Durability ID (SET-B) Freeze-Thaw Durability
1- 13-A-Ref Durable 17- 13-B-Ref non-durable
2- 10-A-HW Durable 18- 10-B-HW Durable
3- 13-A-HW Durable 19- 13-B-HW Durable
4- 16-A-HW Durable 20- 16-B-HW Durable
5- 10-A-ECA-NA non-durable 21- 10-B-ECA-NA non-durable
6- 13-A-ECA-NA non-durable 22- 13-B-ECA-NA non-durable
7- 16-A-ECA-NA non-durable 23- 16-B-ECA-NA Durable
8- 10-A-ECA-NA-FT non-durable 24- 10-B-ECA-NA-FT 2 non-durable
9- 13-A-ECA-NA-FT non-durable 25- 13-B-ECA-NA-FT non-durable
10- 16-A-ECA-NA-FT Durable 26- 16-B-ECA-NA-FT non-durable
11- 10-A-ECA-LA-FT non-durable 27- 10-B-ECA-LA-FT non-durable
12- 13-A-ECA-LA-FT Durable 28- 13-B-ECA-LA-FT Durable
13- 16-A-ECA-LA-FT Durable 29- 16-B-ECA-LA-FT Durable
14- 13-A-ECA-HA-FT Durable 30- 13-B-ECA-HA-FT Durable
15- 16-A-ECA-HA-FT Durable 31- 16-B-ECA-HA-FT Durable
16- 16-A-2xECA-NA-FT non-durable 1 Samples in bold are those used to test the neural network trained with the 24 other sample results. 2 Sample 24 (10-B-ECA-NA-FT) is not considered in this study. In the last report of Task 2, some air-void system characteristics are not determined for this sample due to the high presence of voids (Table 7 of Task 2 report).
10
3.1 Identification of the most influential parameters and combinations of parameters After a first series of tests combining all parameters, the neural network retained parameter no. 3, which presents the lowest error (probably the most reliable parameter for the durability results) (see Table 3). This parameter corresponds to the density of concrete. A further series of calculations was completed to determine the combination of density with another parameter that produces the minimal error.
Table 3 – Test of all parameters (first step)
Parameter retained Error 1 - Compressive strength (psi) 0.79 2 - Absorption (pcf) 1.30
3 - Density (pcf) 0.59 4 - Total air content, considering all voids (%) 0.68 5- Spacing factor, considering all voids (µm) 1.37 6 - Total air content, considering small compaction voids only (%) 1.20
7 - Spacing factor, considering small compaction voids only (µm)
1.37
8 - Total air content, considering small air bubbles only (%) 1.18 9 - Cement content (%) 1.43 10 - Capillary absorption (%) 1.14 11 - Connected voids (%) 1.20 12 - Non-connected voids (%) 1.57 13 - Use of ECA 1.60 14 - Use of high water content 1.44 15 - Use of FT enhancer 1.29 16 - Use of no, low or high dosage of air-entraining admixture 1.18
11
Table 4 – Test of retained parameter (density) and remaining parameters
(second step)
Parameter retained Error 1 - Compressive strength (psi) 0.71 2 - Absorption (pcf) 0.45 3 - Not considered, since we are seeking the best combination of density with other parameters 4 - Total air content, considering all voids (%) 0.70 5 - Spacing factor, considering all voids (µm) 0.78 6 - Total air content, considering small compaction voids only (%) 0.87
7 - Spacing factor, considering small compaction voids only (µm)
0.67
8 - Total air content, considering small air bubbles only (%) 0.76 9 - Cement content (%) 1.15 10 - Capillary absorption (%) 0.61 11 - Connected voids (%) 0.59 12 - Non-connected voids (%) 0.56 13 - Use of ECA 0.67 14 - Use of high water content 0.68 15 - Use of FT enhancer 0.55 16 - Use of no, low or high dosage of air-entraining admixture 0.56
The results presented in Table 4 show that the second parameter, which corresponds to absorption, in combination with parameter no. 3 (density) yields the lowest error (0.45). This error is lower than that obtained with parameter no. 3 only at the first step (0.45 < 0.59). This indicates that the combination of the two parameters is of greater interest than each parameter considered independently. In the continuation, parameter no. 2 (absorption) and parameter no. 3 (density) were retained, and a further series of calculations was completed to determine a third parameter susceptible to combination with the two previously retained parameters.
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Table 5 – Test of retained parameters (density and absorption) with remaining
parameters (third step)
Parameter retained Error 1 - Compressive strength (psi) 0.93 2 - Not considered, since we are seeking the best combination of density and absorption with other parameters 3 - Not considered, since we are seeking the best combination of density and absorption with other parameters 4 - Total air content, considering all voids (%) 0.39 5,- Spacing factor, considering all voids (µm) 0.51 6 - Total air content, considering small compaction voids only (%)
0.24
7 - Spacing factor, considering small compaction voids only (µm) 0.48 8 - Total air content, considering small air bubbles only (%) 1.22 9 - Cement content (%) 0.74 10 - Capillary absorption (%) 0.37 11 - Connected voids (%) 0.45
12 - Non-connected voids (%) 0.22 13 - Use of ECA 0.49 14 - Use of high water content 0.46 15 - Use of FT enhancer 0.46 16 - Use of no, low or high dosage of air-entraining admixture 0.33
Table 5 clearly shows that the combination of parameter no. 2 (absorption), parameter no. 3 (density) and parameter no. 12 (non-connected voids) is very interesting. The error generated by the combination of these three parameters is 0.22, which is lower than the error generated by the combination of density and absorption alone.
13
Table 6 – Test of retained parameters (density, absorption and non-connected
voids) with remaining parameters (fourth step)
Parameter retained Error 1 - Compressive strength (psi) 1.52 2 - Not considered, since we are seeking the best combination of density, absorption and non-connected voids with other parameters 3 - Not considered, since we are seeking the best combination of density, absorption and non-connected voids with other parameters 4 - Total air content, considering all voids (%) 1.59 5 - Spacing factor, considering all voids (µm) 0.33 6 - Total air content, considering small compaction voids only (%)
0.41
7 - Spacing factor, considering small compaction voids only (µm) 0.58 8 - Total air content, considering small air bubbles only (%) 1.77 9 - Cement content (%) 1.35 10 - Capillary absorption (%) 0.38 11 - Connected voids (%) 0.41 12 - Not considered, since we are seeking the best combination of density, absorption and non-connected voids with other parameters 13 - Use of ECA 0.27 14 - Use of high water content 1.17 15 - Use of FT enhancer 0.31
16 - Use of no, low or high dosage of air-entraining admixture
0.20
Table 6 shows that the calculations conducted at the fourth step revealed an interesting combination with a new parameter (parameter no. 16 concerning the dosage of air-entraining agent). The error due to the combination of the three retained parameters and parameter no. 16 is 0.20 (lower than the error generated by the combination of the parameters absorption, density and non-connected voids, which was 0.22). In the calculations for parameter no. 16, a factor of 0 was given for non-air entrained mixtures, a factor of 1 for mixtures with a low dosage of air-entraining admixture, and a factor of 2 for a high dosage of air-entraining admixtures. The next step consisted in conducting a series of calculations for the four retained parameters to determine further combinative possibilities that would yield errors lower than 0.20.
14
Table 7 – Test of retained parameters (density, absorption, non-connected voids and dosage of air-entraining admixture ) with remaining parameters (fifth step)
Parameter retained Error
1 - Compressive strength (psi) 0.83 2 - Not considered, since we are seeking the best combination of density, absorption, non-connected voids and dosage of air-entraining admixture with other parameters 3 - Not considered, since we are seeking the best combination of density, absorption, non-connected voids and dosage of air-entraining admixture with other parameters 4 - Total air content, considering all voids (%) 1.45 5 - Spacing factor, considering all voids (µm) 0.44 6 - Total air content, considering small compaction voids only (%)
0.64
7 - Spacing factor, considering small compaction voids only (µm) 0.88 8 - Total air content, considering small air bubbles only (%) 1.92 9 - Cement content (%) 0.36 10- Capillary absorption (%) 0.74 11 - Connected voids (%) 1.16 12 - Not considered, since we are seeking the best combination of density, absorption, non-connected voids and dosage of air-entraining admixture with other parameters 13 - Use of ECA 1.17 14 - Use of high water content 1.14
15 - Use of FT enhancer 0.35 16 - Not considered, since we are seeking the best combination of density, absorption, non-connected voids and dosage of air-entraining admixture with other parameters
From the results presented in Table 7, we note that an optimization threshold was reached. The error generated by the combination of density, absorption, percentage of the non-connected voids and dosage of air-entraining admixture with other parameters produces a minimal error of 0.35 (with or without the use of freeze-thaw enhancer), but this error is higher than 0.20 (obtained in the fourth step). The neural network cannot be improved. Although the combination including parameter no. 16 (dosage of air-entraining admixture) yielded a lower error, the result was in direct relation to a classification that may not be accurate. It was decided to optimize the neural network with the first three parameters retained (density, absorption and non-connected voids). 3.2 Optimization of hidden neuron number To optimize the neural network based on the three retained parameters, the number of hidden neurons was increased (see Figure 2), with their number varying from 2 to 10. The results were compared with the results of the six samples kept aside to determine the precision of the model (an error was calculated for each possibility and the hidden neuron number yielding the minimal error was retained (see Table 8)).
15
Table 8 – Results analysis of the neural network based on parameters 2, 3 and 12, with number of hidden neurons varying from 2 to 10
Number of neuron layers Error
2 0.21 3 0.19 4 0.24 5 0.23 6 0.27 7 0.20 8 0.19 9 0.23
10 0.19 Although the same error was obtained with three, eight and ten neurons, the neural network formed with three hidden neurons was retained. Two significant reasons justify the choice of the lowest number of hidden neurons: 1. The higher the number of hidden neurons, the higher the number of weights
connecting the input parameters to the hidden neurons. A better network performance is obtained when the relationship between the number of hidden neurons and the number of observations (experimental results) is minimal.
2. As the number of hidden neurons increases, non-linearity increases, and the network
therefore becomes less realistic. The results presented in the Task 2 report are highly variable, even for 5 specimens taken from the same concrete mixture. Increasing the number of neurons would probably cause network overtraining, and might not produce realistic results. A higher number of hidden neurons in a network does not inevitably mean that the network is significantly more performant. Table 8 shows that the errors obtained with 3, 8 and 10 hidden neurons are similar.
A further simulation was conducted with parameters no. 2 (absorption), no. 3 (density) and no. 12 (percentage of non-connected voids), to which we added parameter no. 16 (dosage of air-entraining admixture). The results are presented in Table 9. The neural network showed no improvement when incorporating parameter no. 16.
16
Table 9 – Results analysis of the neural network based on parameters 2, 3, 12 and
16, with number of hidden neurons varying from 2 to 10
Number of neuron layers Error 2 0.28 3 0.24 4 0.20 5 0.45 6 0.22 7 0.23 8 0.30 9 0.23 10 0.23
3.3 Statistical analysis of the results obtained by the neural network optimized with three hidden neurons and based on parameters 2, 3 and 12 (absorption, density and non-connected voids, respectively) Once the network was optimized, the experimental mass loss results were compared with the numerical results calculated by the neural network. Let us recall that part of the experimental results were used for network training (results of 24 specimens presented in Table 10, column 2). The network was subsequently tested on the six samples that were not used for network training. The results presented in Table 10 show that the mass loss results for the 24 samples as calculated by the network approach by 63% the experimental results. It is of note that there is a very close correspondance between the network calculations and the experimental results of the six samples. The network results are almost identical to the experimental results.
17
Table 10 – Comparisons between experimental and numerical results for all specimens
Specimen
identification Experimental results used for network
training 1
Neural Network results
Correspondance between neural
and experimental results
Experimental results used for network
testing
Neural Network results
Correspondance between neural and experimental results
(%) (%) (%) (%) 1 0.32 4.38 No 2 0.12 -0.30 Yes 3 0.12 -0.07 Yes 4 0.20 0.17 Yes 5 9.32 5.60 Yes 6 1.50 3.07 Yes 7 3.17 0.81 No 8 14.8 1.04 Yes 9 1.25 0.42 No 10 0.58 1.26 No 11 1.60 5.28 No 12 0.23 0.01 Yes 13 0.49 0.23 Yes 14 0.47 -0.50 Yes 15 0.28 -0.14 Yes 16 0.91 2.61 No 17 0.81 1.04 No 18 0.29 0.59 Yes 19 0.23 0.94 Yes 20 0.43 1.07 No 21 1.73 1.64 Yes 22 3.90 1.47 Yes 23 0.61 1.33 No 24 25 1.43 1.69 Yes 26 1.72 1.84 Yes 27 1.16 1.02 Yes 28 0.72 0.62 Yes 29 0.38 0.72 Yes 30 0.37 0.85 Yes 31 0.30 0.51 Yes
Statistics (% of success) 63 Statistics (% of success) 100
18
4. Neural Network for Saline Solution Tests 4.1 Identification of the most influential parameters and combinations of parameters The same simulations were conducted for the freeze-thaw test results in saline solution. Table 11 presents the samples used to train the neural network and the six samples used to test the retained network. Tables 12 to 18 present the results of the simulations used to determine the most reliable combinations of parameters. Table 11 – Specimens used for network training and model validation (Testing in
saline solution)
ID (SET-A) 1 Freeze-Thaw Durability1 ID (SET-B) Freeze-Thaw Durability1
1- 13-A-Ref non-durable 17- 13-B-Ref non-durable
2- 10-A-HW Durable 18- 10-B-HW Durable
3- 13-A-HW Durable 19- 13-B-HW Durable
4- 16-A-HW Durable 20- 16-B-HW Durable
5- 10-A-ECA-NA non-durable 21- 10-B-ECA-NA non-durable
6- 13-A-ECA-NA non-durable 22- 13-B-ECA-NA non-durable
7- 16-A-ECA-NA non-durable 23- 16-B-ECA-NA non-durable
8- 10-A-ECA-NA-FT non-durable 24- 10-B-ECA-NA-FT 2 non-durable
9- 13-A-ECA-NA-FT non-durable 25- 13-B-ECA-NA-FT non-durable
10- 16-A-ECA-NA-FT non-durable 26- 16-B-ECA-NA-FT non-durable
11- 10-A-ECA-LA-FT non-durable 27- 10-B-ECA-LA-FT non-durable
12- 13-A-ECA-LA-FT non-durable 28- 13-B-ECA-LA-FT non-durable
13- 16-A-ECA-LA-FT Durable 29- 16-B-ECA-LA-FT Durable
14- 13-A-ECA-HA-FT Durable 30- 13-B-ECA-HA-FT Durable
15- 16-A-ECA-HA-FT non-durable 31- 16-B-ECA-HA-FT Durable
16- 16-A-2xECA-NA-FT non-durable 1 Samples in bold are those used to test the neural network trained with the 24 other sample results. 2 Sample 24 (10-B-ECA-NA-FT) is not considered in this study. In the last report of Task 2, some air-void system characteristics are not determined for this sample due to the high presence of voids (Table 7 of Task 2 report).
19
Table 12 – Test of all parameters (first step)
Parameter retained Error
1 - Compressive strength (psi) 3.15 2 - Absorption (pcf) 7.69 3 - Density (pcf) 5.65 4 - Total air content, considering all voids (%) 7.19 5 - Spacing factor, considering all voids (µm) 8.31 6 - Total air content, considering small compaction voids only (%) 8.49 7 - Spacing factor, considering small compaction voids only (µm) 7.09 8 - Total air content, considering small air bubbles only (%) 6.94 9 - Cement content (%) 8.46 10 - Capillary absorption (%) 5.92 11 - Connected voids (%) 6.51 12 - Non-connected voids (%) 8.32 13 - Use of ECA 9.72 14 - Use of high water content 8.76 15 - Use of FT enhancer 7.94 16 - Use of no, low or high dosage of air-entraining admixture 8.33
The first step consists of creating a neural network for each parameter in Table 2. These simulations reveal that compressive strength is the most reliable parameter for the mass loss results (see Table 12). However, the minimal error generated was significant (3.15).
Table 13 – Test of retained parameter (compressive strength) with remaining parameters (second step)
Parameter retained Error
1 - Not considered, since we are seeking the best combination of compressive strength with other parameters 2 - Absorption (pcf) 1.97 3 - Density (pcf) 3.86 4 - Total air content, considering all voids (%) 2.12 5 - Spacing factor, considering all voids (µm) 3.67 6 - Total air content, considering small compaction voids only (%) 2.77 7 - Spacing factor, considering small compaction voids only (µm) 2.24 8 - Total air content, considering small air bubbles only (%) 2.65 9 - Cement content (%) 5.15 10 - Capillary absorption (%) 2.67 11 - Connected voids (%) 2.84
12 - Non-connected voids (%) 1.70 13 - Use of ECA 5.27 14 - Use of high water content 4.18 15 - Use of FT enhancer 2.38 16 - Use of no, low or high dosage of air-entraining admixture 3.84
20
The results presented in Table 13 show that parameter no. 12 (non-connected voids) combined with compressive strength is more reliable than either parameter considered individually, and that the combination yields an error of 1.70 (lower than 3.15). Table 14 – Test of retained parameters (compressive strength and non-connected
voids) with remaining parameters (third step)
Parameter retained Error 1 - Not considered, since we are seeking the best combination of compressive strength and non-connected voids with other parameters 2 - Absorption (pcf) 3.86 3 - Density (pcf) 3.07 4 - Total air content, considering all voids (%) 3.49 5 - Spacing factor, considering all voids (µm) 1.66 6 - Total air content, considering small compaction voids only (%) 2.45 7 - Spacing factor, considering small compaction voids only (µm) 1.82 8 - Total air content, considering small air bubbles only (%) 1.82 9 - Cement content (%) 4.67 10 - Capillary absorption (%) 1.67 11 - Connected voids (%) 2.34 12 - Not considered, since we are seeking the best combination of compressive strength and non-connected voids with other parameters 13 - Use of ECA 3.31 14 - Use of high water content 2.30 15 - Use of FT enhancer 1.48 16 - Use of no, low or high dosage of air-entraining admixture 5.08
Once parameters no. 1 (compressive strength) and no. 12 (non-connected air voids) were retained, a new network was created. The results of the new simulations are presented in Table 14, where a new combination of compressive strength, non-connected voids and use of FT enhancer yielded an error of 1.48, lower than the 1.70 error obtained in the previous simulation. Since the error decreased, simulations with the three retained parameters (1, 12 and 15) were carried out. Table 15 shows results lower than 1.48 with two combinations. The first combination (error of 0.75) was based on compressive strength, non-connected voids, use of FT enhancer and use of high water content. The second possible combination (error of 0.79) was composed of compressive strength, non-connected voids, use of FT enhancer and percentage of connected voids.
21
Table 15 – Test of retained parameters (compressive strength, non-connected
voids and use of FT enhancer) with remaining parameters (fourth step)
Parameter retained Error 1 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids and use of FT enhancer with other parameters 2 - Absorption (pcf) 2.69 3 - Density (pcf) 1.67 4 - Total air content considering all voids (%) 8.84 5 - Spacing factor considering all voids (µm) 2.60 6 - Total air content considering small compaction voids only (%) 6.52 7 - Spacing factor considering small compaction voids only (µm) 3.13 8 - Total air content considering small air bubbles only (%) 1.29 9 - Cement content (%) 10.77 10 - Capillary absorption (%) 0.95 11 - Connected voids (%) 0.79 1
12 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids and use of FT enhancer with other parameters 13 - Use of ECA 2.27
14 - Use of high water content 0.75 15 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids and use of FT enhancer with other parameters 16 - Use of no, low or high dosage of air-entraining admixture 4.38
1 Another simulation will be made with parameter 11 instead of parameter 14 (See tables 15, 16 and 17). Simulations with the first combination revealed that we reached an optimization limit (see Table 16). The minimal error generated was 1.26, which is higher than the previous simulation error (0.75). Nevertheless, simulations with the second combination were very interesting. The results presented in Table 17 show that combining parameter no. 10 (capillary absorption) with compressive strength, non-connected voids, use of FT enhancer and percentage of connected voids generates an error of 0.74, which is lower than the 0.79 error yielded by the previous combination. An optimization limit was reached with this new combination. Further simulations seeking other combinations led to an error higher than 0.74 (see results in Table 18).
22
Table 16 – Test of retained parameters (compressive strength, non-connected voids, FT enhancer and water content) with remaining parameters (fifth step)
Parameter retained Error
1 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and use of high water content with other parameters 2 - Absorption (pcf) 2.35 3 - Density (pcf) 2.07 4 - Total air content, considering all voids (%) 7.40 5 - Spacing factor, considering all voids (µm) 2.30 6 - Total air content, considering small compaction voids only (%) 5.94 7 - Spacing factor, considering small compaction voids only (µm) 2.11 8 - Total air content, considering small air bubbles only (%) 1.82 9 - Cement content (%) 6.45 10 - Capillary absorption (%) 1.33 11 - Connected voids (%) 1.50 12 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and use or not of high water content with other parameters 13 - Use of ECA 3.07 14 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and use or not of high water content with other parameters 15 - Not considered, we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and use or not of high water content with other parameters 16 - Use of no, low or high dosage of air-entraining admixture
1.26
23
Table 17 – Test of retained parameters (compressive strength, non-connected voids, use of FT enhancer and connected voids) with remaining parameters
Parameter retained Error
1 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and connected voids with other parameters 2 - Absorption (pcf) 1.04 3 - Density (pcf) 2.35 4 - Total air content, considering all voids (%) 10.89 5 - Spacing factor, considering all voids (µm) 1.51 6 - Total air content, considering small compaction voids only (%) 18.12
7 - Spacing factor, considering small compaction voids only (µm) 1.72 8 - Total air content, considering small air bubbles only (%) 1.01 9 - Cement content (%) 10.43
10 - Capillary absorption (%) 0.74 11 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and Connected voids with other parameters
12 - Not considered, we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and Connected voids with other parameters 13 - Use of ECA 3.76 14 - Use of high water content 1.54 15 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer and Connected voids with other parameters 16 - Use of no, low or high dosage of air-entraining admixture 3.45
24
Table 18 – Test of retained parameters (compressive strength, non-connected voids, use of FT enhancer, connected voids and capillary absorption) with
remaining parameters
Parameter retained Error 1 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, use of FT enhancer, connected voids and capillary absorption with other parameters 2 - Absorption (pcf) 1.75 3 - Density (pcf) 2.07 4 - Total air content, considering all voids (%) 20.43 5 - Spacing factor, considering all voids (µm) 1.35 6 - Total air content, considering small compaction voids only (%)
5.52
7 - Spacing factor, considering small compaction voids only (µm) 3.26 8 - Total air content, considering small air bubbles only (%) 1.50 9 - Cement content (%) 9.91 10 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, presence or not of FT enhancer, connected voids and capillary absorption with other parameters 11 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, presence or not of FT enhancer, connected voids and capillary absorption with other parameters
12 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, presence or not of FT enhancer, connected voids and capillary absorption with other parameters 13 - Use of ECA 4.12 14 - Use of high water content 1.85 15 - Not considered, since we are seeking the best combination of compressive strength, non-connected voids, presence or not of FT enhancer, connected voids and capillary absorption with other parameters 16 - Use of no, low or high dosage of air-entraining admixture 3.25
Three parameter combinations seem to be promising. They are: 1. A combination of parameters no. 1 (compressive strength), no. 10 (capillary
absorption), no. 11 (connected voids), no. 12 (non-connected voids), and no. 15 (use of FT enhancer).
2. A combination of parameters no. 1 (compressive strength), no. 12 (non-connected voids), no. 14 (use of high water content) and no. 15 (use of FT enhancer).
3. A combination of parameters no. 1 (compressive strength), no. 11 (connected voids), no. 12 (non-connected voids), and no. 15 (use of FT enhancer).
4.2 Optimal number of hidden neurons The last step in optimizing the neural network based on the above simulations was to increase the number of hidden neurons from 2 to 10. The results were compared with
25
the six samples that were kept aside to determine the precision of the model. An error was calculated for each possibility and the number of hidden neurons yielding the minimal error was retained (see Tables 19, 20 and 21). Three parameter combinations were conserved: (1) a combination of parameters 1, 10, 11, 12 and 15, (2) a combination of parameters 1, 12, 14 and 15, and (3) a combination of parameters 1, 11, 12 and 15.
Table 19 – Results analysis of neural network based on parameters 1, 10, 11, 12 and 15, with number of hidden neurons varying from 2 to 10
Number of hidden neurons Error
2 0.76 3 0.74 4 0.74 5 0.73 6 0.74 7 0.74 8 0.74 9 0.72 10 0.74
Table 20 – Results analysis of neural network based on parameters 1, 12, 14 and
15, with number of hidden neurons varying from 2 to 10
Number of hidden neurons Error 2 0.77 3 0.75 4 0.76 5 0.80 6 0.78 7 0.76 8 0.78 9 0.75 10 0.77
26
Table 21 – Results analysis of neural network based on parameters 1, 11, 12 and 15, with number of hidden neurons varying from 2 to 10
Number of hidden neurons Error
2 0.83 3 0.76 4 0.75 5 0.79 6 0.74 7 0.73 8 0.73 9 0.73 10 0.73
4.3 Statistical analysis of the results obtained by the neural network optimized with four hidden neurons and based on parameters 1, 11, 12 and 15 (compressive strength, connected voids, non-connected voids and use or not of FT enhancer, respectively) For the freeze-thaw durability test in saline solution, the neural network obtained with parameters no. 1 (compressive strength), no. 11 (connected voids), no. 12 (non-connected voids) and no. 15 (use of FT enhancer) was considered. A comparison between the experimental results and the neural network results is presented in Table 21. When the retained neural network was applied to the 24 samples (used for training) and the 6 samples (used for testing), the numerical results approach the experimental results by 79% and 50%, respectively. It is noteworthy that building a neural network with only 30 results is not an easy task, particularly when 9 results were estimated and considering that the tests were terminated after 25 or 35 cycles. More samples and more results would certainly lead to a more complete neural network and more accurate results.
27
Table 22 – Comparisons between experimental
and numerical results for all specimens
Specimen identification
Experiment results used for network
training 1
Neural Network results
Correspondance between neural
and experimental results
Experimental results used for network testing
Neural Network results
Correspondance between neural
and experimental results
(%) (%) (%) (%) 1 1.26 9.55 Yes 2 0.41 5.23 No 3 0.29 1.46 No 4 0.49 4.21 No 5 37.28 10.91 Yes 6 6 21.72 Yes 7 12.68 4.06 Yes 8 59.2 37.53 Yes 9 5 18.63 Yes 10 8.46 15.49 Yes 11 1.28 12.87 Yes 12 1.32 0.72 No 13 0.78 1.18 No 14 0.65 2.04 No 15 2.55 1.90 Yes 16 3.64 13.20 Yes 17 3.24 11.55 Yes 18 0.98 3.66 No 19 0.54 -0.51 Yes 20 0.34 -1.46 Yes 21 6.92 6.64 Yes 22 15.6 2.32 Yes 23 2.44 0.61 No 24 25 14.27 5.33 Yes 26 6.88 5.71 Yes 27 57.36 23.23 Yes 28 4.53 5.92 Yes 29 0.87 -0.03 Yes 30 1 0.99 Yes 31 0.67 0.69 Yes
Statistics (% of success) 79 Statistics (% of success)
50
1 Experimental results in bold are assumed to be equal to 4 times the mass loss of the same specimens in tap water. It is noteworthy that the tests were terminated before reaching 40 cycles in saline solution.
28
5. Concluding Remarks The freeze-thaw durability of dry concrete is a highly complex phenomenon. It was very difficult to find correlations between the mass loss results of dry concrete and the parameters that we considered the most significant. For better analysis, we used the Neural Network Method. This method has the advantage of taking into account the potential non-linearity between input parameters and results. For each test condition (in tap water or saline solution), a neural network was built and subsequently trained with a portion of the results. The remaining portion of the results was used to determine the minimal error between the calculated and experimental results. In the case of the tap water tests, the optimized neural network was composed of three (3) hidden neurons, and was based on the following three parameters: absorption, density and non-connected voids. The most interesting results were obtained with the six samples that were not used to train the network, but rather to test it. The optimized neural network allowed us to precisely estimate dry concrete performance subjected to freeze-thaw cycles in tap water (see Table 10). In the case of concrete subjected to freeze-thaw cycles in saline solution, the task was more difficult than for the tap water testing. Testing of several samples was stopped after a certain number of cycles, and the mass loss was therefore unknown for these samples. To overcome this problem, mass loss in saline solution for these samples was estimated at 4 times the mass loss during the tap water testing. It was then difficult to train networks with estimated values. However, when we tested the optimized neural network with four (4) hidden neurons, using as input parameters: compressive strength, connected voids, non-connected voids and use of FT enhancer, we approached the experimental results by 50% (see Table 22). We believe that we have developed a tool for the classification of durable and non-durable concrete that, at this stage, still requires improvement. This improvement would be made possible by taking into account other experimental tests and by increasing the database to make it more objective. This should be achieved in Tasks 4 and 5, where we will accumulate the data and reperform the simulations so as to develop at the end of this study two neural networks for testing in tap water and saline solution. These two networks would probably be programmed in the form of a small, accessible software that could easily be used by dry concrete producers.
IMPROVING THE FROST DURABILITY OF CONCRETE MASONRY PRODUCTS
Final Report
Task 4 – Laboratory Validation of Durability and Assessment of Frost Durability of 13 Existing Masonry Units
TAP WATER
Project SEM00110
Presented to:
Robert D. Thomas National Concrete Masonry Association
2302 Horse Pen Road Herndon, VA 20171-3499
USA
February 2006
ii
LIMITED LIABILITY STATEMENT
THE RESULTS PRESENTED HERE ARE PRELIMINARY AND UNREVIEWED AND SHOULD BE TREATED AS SUCH. THESE DATA ARE PROVIDED FOR INFORMATION PURPOSES ONLY AND ARE PRESENTLY BEING REVIEWED AND ANALYZED BY S.E.M. EXPERTS FOR PUBLICATION IN A FINAL REPORT. S.E.M. ASSUMES NO RESPONSIBILITY OR LIABILITY FOR THE ACCURACY OR RELIABILITY OF THESE DATA. NO INFORMATION OBTAINED FROM THESE DATA SHALL CREATE ANY WARRANTY WHATSOEVER. CLIENT HEREBY ACKNOWLEDGES AND AGREES THAT THE USE OF ANY INFORMATION CONTAINED IN THIS REPORT IS AT CLIENT'S OWN DISCRETION AND RISK.
iii
TABLE OF CONTENTS
Limited Liability Statement................................................................................................................................ ii Table of contents ............................................................................................................................................... iii 1.0. OBJECTIVES AND PROJECT DESCRIPTION........................................................................... 1 2.0. BACKGROUND INFORMATION .................................................................................................. 2 3.0. IMPROVING THE FROST DURABILITY INDEX (TASK 3) .................................................... 3
3.1. Theoretical background: the neural network ................................................................................... 3 3.2. Procedure to establish the neural network....................................................................................... 6 3.3. Neural Network for the freeze-thaw test in tap water...................................................................... 7
3.3.1. Identification of the most influential parameters and parameter combinations .......................... 9 3.3.2 Optimal number of hidden neurons .......................................................................................... 14 3.3.3 Statistical analysis of the results given by the neural network optimized with four hidden neurons and based on parameter nos. 1, 2, 6, and 12 (respectively, compressive strength, absorption, total air content, and percentage of the non-connected voids).......................................................................... 15
4.0. PREDICTION OF FROST DURABILITY OF 13 NEW MASONRY UNITS .......................... 17 4.1. Mixture properties ......................................................................................................................... 17 4.2. Frost durability results based on the neural network and discussion............................................. 21
5.0. DISCUSSION AND CONCLUDING REMARKS........................................................................ 23
1.0. OBJECTIVES AND PROJECT DESCRIPTION The main objective of this project was to develop a predictive index for the frost durability of Segmental Retaining Wall Units (SRW units). SWR units are manufactured concrete masonry units used to build earth retaining walls. Frost durability is one of many properties that are important to manufacturers and users of SRW units. The frost durability index would be determined with the help of a neural network which uses a number of physical properties of the SRW units as inputs. The index could be used by masonry producers to improve the durability of their products. Development of this index could also help establish acceptable limits for the degradation of masonry units tested in tap water and salt solution. The study is divided into six different tasks: • Task 1 - Literature Survey • Task 2 - Data Analysis and Additional Testing • Task 3 - Development of a Frost Durability Index • Task 4 - Laboratory Validation of the Frost Durability Index • Task 5 - Assessment of the Frost Durability of Existing Masonry Units • Task 6 – Preparation of the Final Report. The first five tasks address specific aspects of the investigated problem. The project was designed to optimize the use of existing data on the frost durability of masonry units generated by the National Concrete Masonry Association (NCMA). Part one of the study, a comprehensive search of the scientific and technical literature, was conducted in 2001. The Task 1 Final Report was submitted to NCMA on August 7, 2001. Part two, data analysis and additional testing, was conducted in 2003. The Task 2 Final Report was submitted to NCMA on January 31, 2004. Part three, developing a classification tool for durable and non-durable concrete using a neural network, was submitted to NCMA in February 2004. Following a review of the Task 3 Report, NCMA representatives requested some modifications to the proposed neural networks. The present report presents the improvements to the neural networks developed in Task 3 for tap water only. A subsequent report will present the neural network for prediction in salt solution. The main requested modifications to the neural network were to ignore certain parameters that are either not easily tracked by the producer (e.g., absolute water content of the concrete mix), not easily discernable from examining the finished product (e.g., cement
2
content, use of admixtures), or not inherent to the concrete matrix (e.g., concrete density). In the latter case, concrete density is not an inherent property because it is dependent on the density of the aggregates used (i.e., lightweight aggregates will yield a lower density product than normal weight aggregates even if the void structure in the concrete matrix is exactly the same). On the other hand, the percentage of non-connected voids, for example, is an inherent property because it describes the concrete matrix and is not dependent on constituent properties. Therefore, it is a parameter that can be used by the neural networks. The neural networks were therefore trained with parameters such as density, cement content, water content, and the use of admixtures ignored. The complete list of ignored parameters is shown in Table 1. This report begins with a presentation of the revised neural network for the tap water condition. The results on Task 4 – Validation of the Frost Durability Index are then presented. NCMA provided 13 new sets of SRW unit specimens for frost durability prediction using the revised neural network. Along with providing the specimens, NCMA determined the physical properties of the SRW units including compressive strength, absorption and density. NCMA also conducted the ASTM C 1262 freeze/thaw testing on the 13 sets of specimens and provided the results which were subsequently compared with the predicted results from the neural network to determine the accuracy of the neural network predictions. 2.0. BACKGROUND INFORMATION Initially, this study was to be primarily based on a series of readily available parameters provided by a previous study conducted by NCMA in 1998-19991. Thirty sets of concrete masonry units were manufactured and tested to determine the effects of various production parameters on freeze-thaw durability. Test variables included cement type (Type I and Type III), mixture water content, use of various chemical admixtures (water repellent, plasticizer), aggregate gradation, and compaction time. Compressive strength, absorption, and density of all mixtures were determined by NCMA. Frost durability tests were also carried out by NCMA (in tap water and salt solution) according to ASTM C 1262. In 2001, NCMA decided that, rather than using results provided by the 1998-1999 investigation, the study would be based on test results from a completely new set of units. Consequently, 31 different mixtures were manufactured in April 2002 by two different producers. Many test variables were also modified. All changes made to the original research program as well as the new concrete production data were provided in a previous report (see Task 2 Report – Data Analysis and Additional Testing – January 31, 2004).
1 Memorandum (1998), Preliminary Results of NCMA Phase 3 Durability Research. The NCMA Durability Task Group, 23 p.
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For all 31 concrete mixtures, compressive strength, absorption, and density were determined by NCMA’s Research & Development Laboratory. Frost durability tests were also carried out by NCMA (in tap water and salt solution) according to ASTM C 1262. Results were made available to us in January 2003. Pressure-saturation tests were conducted by SEM in its laboratory to determine the volume of non-connected voids. These results, used in an earlier report (see Task 3 Report – Elaboration of a Frost Durability Index – February, 2004) to produce the initial neural network, were reused to train the revised neural network. 3.0. IMPROVING THE FROST DURABILITY INDEX (TASK 3) In the current study, the frost durability index developed in Task 3 was improved. Certain parameters considered in Task 3 were ignored in Task 4, since they are either not easily tracked by the producer, not easily discernable from examining the finished product, or not inherent to the concrete matrix. These parameters include density, use of efflorescence controlling admixture (ECA), water content, use of air entraining admixture (AEA), and use of freeze/thaw enhancing admixture (FT enhancer). As a first step, traditional mathematical tools were used to determine the relationship between mass loss and the different parameters studied (compressive strength, absorption, spacing factor, etc.) However, no linear relationship was found between the different parameters and the frost durability results. Consequently, a more sophisticated method to determine non-linear relationships between the different parameters or combinations of parameters and freeze-thaw results was used. This method, known as the Neural Network method, is described below. 3.1. Theoretical background: the neural network The neural network is composed of simple processing entities (called nodes, units, or neurons) operating in parallel. As in the animal brain, the network function is determined largely by the connections between the neurons. A neural network can be trained to perform a particular function by adjusting the strengths (or weights) of these connections. Briefly, neural networks are trained so that a single output value is generated from all the input values applied to the neuron. As shown in Figure 1, batch training of a neural network proceeds by adjusting the weight connections and vectors based on a set of inputs presented to the neuron.
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Figure 1 – Neural network architecture
Multi-layer networks were used to analyze the freeze-thaw results in tap water with respect to two functions: a non-linear sigmoid function between input data and the hidden layer of neurons, and a linear function between the hidden neurons and the output neuron (see Figure 2). The neural network method was used to identify the most reliable parameters and combinations of parameters for the durability results. The eleven parameters tested are presented in Table 1.
Neural network: neurons and the
weighted unidirectional connections between
them
Input Output Error = difference
between target and actual
output
Target
Weight adjustment
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Figure 2 – Neural network used in this study
PR
P2
P1
Input Data
Hidden Neurons
Output Data
W11
W21
WRn
b1
b2
bn-1
bn
First layer output is calculated by sigmoid function.
Second layer output is calculated by linear function.
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Table 1 – Parameters and measurement units
Parameter tested Unit
1 - Compressive strength psi
2 - Absorption pcf
3 - Density pcf
4- Total air content considering all voids %
5- Spacing factor considering all voids µm
6- Total air content considering small compaction voids only %
7- Spacing factor considering small compaction voids only µm
8- Total air content considering small air bubbles only %
9 - Cement content %
10 - Capillary absorption %
11 - Connected voids %
12 - Non-connected voids %
13 - Use of ECA 1 if used or 0 if not
14 - Use of high water content 1 if used or 0 if not
15 - Use of FT Enhancer 1 if used or 0 if not
16 - Use of no, low, or high dosage of air-entraining admixture 0 if no air, 1 if low air, 2 if high air Note: Crossed-out parameters were ignored in the development of the revised neural network. 3.2. Procedure to establish the neural network The neural network development followed a simple procedure, comprising the following six steps: 1. A neural network was formed and trained with the input data (parameter values
presented in Table 1 and percent mass loss results for 24 of the 31 samples). 2. For each input parameter, the network predicted durability results for the 6 test samples
(in bold in Table 2, page 7). The 6 samples were taken from the list of 31 samples presented in our Task 2 Report – Data Analysis and Additional Testing, dated January 31, 2004).
3. Numerical results were compared with the experimental results obtained on the
6 samples. Simulation errors were calculated. 4. The parameter that produced the minimum error was retained.
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5. A second simulation was then performed to arrive at a combination of the parameter
retained in step 4 and the remaining series of parameters to determine if there was a second parameter that, when combined with the original retained parameter, generated an error lower than that obtained in step 4. The parameter that produced the minimum error was then retained along with the original retained parameter from step 4.
6. The process was then repeated with the two retained parameters from step 5 being
combined with the remained parameters and the errors resulting from each combination being calculated to determine of there was a minimum error that was lower than the error obtained in step 5. If there was a parameter that fit this criterion, then it was retained and the process was continually repeated until the error obtained was equal to or higher than the error the previous step. The process was then stopped.
7. Once the more reliable parameters had been retained from the process described in
steps 1 to 6, the number of hidden neurons was increased, varying from 2 to 10. Error was calculated for each possibility and the number of hidden neurons yielding the minimum error was retained.
3.3. Neural Network for the freeze-thaw test in tap water Ten parameters were used as input data to determine the frost durability index for the specimens tested in tap water (see Table 1). The output data represent the mass loss (%) in tap water after 100 cycles. As shown in Table 2, our neural network was formed and trained using the results on 24 samples (concrete mixtures). Six samples were retained to test the accuracy of our neural network. One sample (10-B-ECA-NA-FT) was discarded from the study owing to a substantial amount of missing data. The 24 samples used to train the network and the 6 samples (in red/bold) used to test the retained neural network are presented in Table 2.
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Table 2 – Specimens tested in tap water and used for network training and validation
ID (SET-A) 1 Freeze-thaw durability ID (SET-B) Freeze-thaw durability
1- 13-A-Ref Durable 17- 13-B-Ref Not durable
2- 10-A-HW Durable 18- 10-B-HW Durable
3- 13-A-HW Durable 19- 13-B-HW Durable
4- 16-A-HW Durable 20- 16-B-HW Durable
5- 10-A-ECA-NA Not durable 21- 10-B-ECA-NA Not durable
6- 13-A-ECA-NA Not durable 22- 13-B-ECA-NA Not durable
7- 16-A-ECA-NA Not durable 23- 16-B-ECA-NA Durable
8- 10-A-ECA-NA-FT Not durable 24- 10-B-ECA-NA-FT 2 Not durable
9- 13-A-ECA-NA-FT Not durable 25- 13-B-ECA-NA-FT Not durable
10- 16-A-ECA-NA-FT Durable 26- 16-B-ECA-NA-FT Not durable
11- 10-A-ECA-LA-FT Not durable 27- 10-B-ECA-LA-FT Not durable
12- 13-A-ECA-LA-FT Durable 28- 13-B-ECA-LA-FT Durable
13- 16-A-ECA-LA-FT Durable 29- 16-B-ECA-LA-FT Durable
14- 13-A-ECA-HA-FT Durable 30- 13-B-ECA-HA-FT Durable
15- 16-A-ECA-HA-FT Durable 31- 16-B-ECA-HA-FT Durable
16- 16-A-2xECA-NA-FT Not durable 1 Samples in red/bold were used to test the neural network previously trained with the 24 other sample results. 2 Sample 24 (10-B-ECA-NA-FT) was not considered in this study. In the earlier Task 2 Report, certain air-void system characteristics were not determined for this sample due to high void content (Table 7 of Task 2 Report).
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3.3.1. Identification of the most influential parameters and parameter combinations Following the initial test series combining all parameters, the Neural Network determined that parameter no. 1 presents the lowest error (probably the most reliable parameter for durability results) (see Table 3). This parameter corresponds to compressive strength. A second series of calculations was completed to determine the combination of compressive strength and another parameter that produces the minimum error.
Table 3 – Test using all parameters (step one)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 0.57 2 - Absorption (pcf) 0.86 3 - Density (pcf) 4 - Total air content considering all voids (%) 0.66 5 - Spacing factor considering all voids (µm) 0.86 6 - Total air content considering small compaction voids only (%) 0.66 7 - Spacing factor considering small compaction voids only (µm) 0.86 8 - Total air content considering small air bubbles only (%) 0.62 9 - Cement content (%) 10 - Capillary absorption (%) 0.73 11 - Connected voids (%) 0.78 12 - Non-connected voids (%) 0.87 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
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Results presented in Table 4 show that the second parameter, which corresponds to absorption (parameter no. 2), in combination with parameter no. 1 (compressive strength) yields the lowest error (0.38). Because this error is lower than the previous one (0.38 < 0.57), the two parameters combined are more useful than either parameter independently. In the continuation, parameter no. 1 (compressive strength) and parameter no. 2 (absorption) were retained, and a further calculation series was completed to determine the best choice for the third combined parameter.
Table 4 – Test using first retained parameter (compressive strength) and remaining parameters (step two)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi)
2 - Absorption (pcf) 0.38 3 - Density (pcf) 4 - Total air content considering all voids (%) 0.50 5 - Spacing factor considering all voids (µm) 0.60 6 - Total air content considering small compaction voids only (%) 0.50 7 - Spacing factor considering small compaction voids only (µm) 0.57 8 - Total air content considering small air bubbles only (%) 0.61 9 - Cement content (%) 10 - Capillary absorption (%) 0.59 11 - Connected voids (%) 0.58 12 - Non-connected voids (%) 0.56 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
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Table 5 clearly shows that the combination of parameter no. 1 (compressive strength), parameter no. 2 (absorption), and parameter no. 6 (total air content considering small compaction voids only) is very useful. The error generated by the combination of these three parameters is 0.29, lower than that generated by the combination of just compressive strength and absorption (0.38 – see Table 4).
Table 5 – Test using two retained parameters (compressive strength and absorption) and remaining parameters (step three)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 2 - Absorption (pcf) 3 - Density (pcf) 4 - Total air content considering all voids (%) 0.33 5 - Spacing factor considering all voids (µm) 0.43
6 - Total air content considering small compaction voids only (%) 0.29 7 - Spacing factor considering small compaction voids only (µm) 0.43 8 - Total air content considering small air bubbles only (%) 0.40 9 - Cement content (%) 10 - Capillary absorption (%) 0.39 11 - Connected voids (%) 0.39 12 - Non-connected voids (%) 0.30 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
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Table 6 shows that the calculation performed in step 4 reveals a useful combination with a new parameter (parameter no. 12 – Non-connected voids). The error produced by the combination of the three retained parameters and parameter no. 12 is 0.24 (lower than that generated by the combination of density, absorption, and non-connected voids, at 0.29 – see Table 4). The next step was to perform a series of calculations with the four retained parameters to determine any further combinations yielding errors lower than 0.20. Table 6 – Test using three retained parameters (compressive strength, absorption, and
total air content) and remaining parameters (step four)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 2 - Absorption (pcf) 3 - Density (pcf) 4 - Total air content considering all voids (%) 0.25 5 - Spacing factor considering all voids (µm) 0.90 6 - Total air content considering small compaction voids only (%) 7 - Spacing factor considering small compaction voids only (µm) 0.64 8 - Total air content considering small air bubbles only (%) 0.42 9 - Cement content (%) 10 - Capillary absorption (%) 0.27 11 - Connected voids (%) 0.28
12 - Non-connected voids (%) 0.24 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
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From the results presented in Table 7, it appears that a threshold of optimization was reached. The error generated by the combination of compressive strength, absorption, total air content, percentage of unconnected voids, and connected voids gives a minimum error of 0.24, which is identical to the neural network result using only 4 parameters. Since the neural network could not be further improved, it was decided to establish the first four parameters retained (compressive strength, absorption, total air content, and percentage of non-connected voids) as the optimal set. This new neural network produces a higher error than that generated in a previous analysis (0.24 in Task 4 compared to 0.20 in Task 3 – see report Task 3 dated February 2004). Moreover, the new model requires an additional parameter. Table 7 – Test using four retained parameters (compressive strength, absorption, total
air content, and non-connected voids) and remaining parameters (fifth step)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 2 - Absorption (pcf) 3 - Density (pcf) 4 - Total air content considering all voids (%) 0.28 5 - Spacing factor considering all voids (µm) 0.69 6 - Total air content considering small compaction voids only (%) 7 - Spacing factor considering small compaction voids only (µm) 0.32 8 - Total air content considering small air bubbles only (%) 0.42 9 - Cement content (%) 10 - Capillary absorption (%) 0.28
11 - Connected voids (%) 0.24 12 - Non-connected voids (%) 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
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3.3.2 Optimal number of hidden neurons To optimize the neural network based on the four retained parameters, the number of hidden neurons was increased (see Figure 2), varying from 2 to 10. The results were compared with the results of the 6 samples selected to evaluate the accuracy of the model (errors were calculated for each possibility and the number of hidden neurons yielding the smallest error was retained (see Table 8)).
Table 8 – Results analysis of neural network based on parameter nos. 1, 2, 6, and 12
Number of neuron layers Error 2 0.31 3 0.26 4 0.24 5 0.25 6 0.25 7 0.28 8 0.30 9 0.23
10 0.19 The neural network formed with 4 hidden neurons was retained, although a higher number of neurons yielded slight improvements. Two significant reasons justify the choice of the lowest number of hidden neurons: 1. The higher the number of hidden neurons, the higher the number of weights connecting
the input parameters to the hidden neurons. Better network performance is obtained when the relationship between the number of hidden neurons and the number of observations (experimental results) is minimal.
2. Non-linearity increases with the increase in number of hidden neurons, rendering the
network less realistic. Results presented in the Task 2 Report are quite variable, even for 5 specimens made from the same concrete mixture. Increasing the number of neurons would probably result in overtraining of the network and potentially unrealistic results. A greater number of hidden neurons in a network does not necessarily mean a better network.
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3.3.3 Statistical analysis of the results given by the neural network optimized with four hidden neurons and based on parameter nos. 1, 2, 6, and 12 (respectively, compressive strength, absorption, total air content, and percentage of the non-connected voids) Once the neural network was optimized, experimental mass loss results were compared with the numerical results calculated by the neural network. As described above, certain experimental results were used for network training (24 specimens, Table 9, column 2). The network was subsequently tested for the six samples not used for network training. The results, presented in Table 9, show that mass loss results calculated by the neural network reasonably approximate experimental results on the 24 samples at 79%. It should be noted that the new neural network has a better success rate than that presented in Task 3 (which had a success rate of 63% in 2004) even though this new neural netowkr produces a higher network error (0.24 for Task 4 compared to 0.20 for Task 3). In addition, the very close correspondence between neural network calculations and experimental results on the six samples is noteworthy, with the neural network duplicating experimental results at 100%. Correspondence in Table 8 is defined by mass loss predicted by the neural network. Correspondence is obtained when predicted and experimental mass losses indicate frost durability based on the 1% limit of mass loss.
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Table 9 – Experimental versus numerical results for all specimens
Specimen identification
Experimental results used for network
training 1
Neural Network results
Correspondence between neural and
experimental results
Experimental results used for network testing
Neural Network results
Correspondence between neural and
experimental results
(%) (%) (%) (%) 1 0.32 1.13 No 2 0.12 1.10 No 3 0.12 0.22 Yes 4 0.20 -0.01 Yes 5 9.32 4.61 Yes 6 1.50 1.86 Yes 7 3.17 0.65 No 8 14.8 5.01 Yes 9 1.25 1.28 Yes
10 0.58 0.88 Yes 11 1.60 1.90 Yes 12 0.23 0.28 Yes 13 0.49 0.33 Yes 14 0.47 0.39 Yes 15 0.28 0.31 Yes 16 0.91 0.50 Yes 17 0.81 1.22 No 18 0.29 0.79 Yes 19 0.23 0.37 Yes 20 0.43 0.45 Yes 21 1.73 1.39 Yes 22 3.90 1.16 Yes 23 0.61 1.01 No 24 25 1.43 1.27 Yes 26 1.35 1.00 Yes 27 1.16 2.04 Yes 28 0.72 0.70 Yes 29 0.38 0.35 Yes 30 0.37 0.74 Yes 31 0.30 0.46 Yes
Statistics (% success) 79 Statistics (% success) 100
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4.0. PREDICTION OF FROST DURABILITY OF 13 NEW MASONRY UNITS 4.1. Mixture properties Once the neural network was reviewed and improved (see earlier section), NCMA sent to our laboratories a new set of 13 masonry concrete mixes. Technical data on these mixes were provided by NCMA, including compressive strength, absorption, and density. Other properties were determined at our laboratories with supplementary testing, as follows: ♦ Determination of the air void characteristics – ASTM C457 ♦ Pressure-saturation test. Results of the tests performed by NCMA (see Table 10) indicate that the 13 new masonry concrete mixes were made from a wide range of concrete mixtures. Compressive strength ranged from 3690 psi to 12440 psi, while absorption ranged from 3.3 pcf to 12.6 pcf. Density of the masonry units ranged from 129.4 pcf to 149.0 pcf. These masonry samples provided a good evaluation of the neural network to predict the frost durability of masonry mixes.
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Table 10 – NCMA results
ID Compressive Strength Absorption Density
SEM CODE NCMA Job # (psi) (MPa) (pcf) (%) (pcf) (kg/m3)
A 04-420-02 6970 48.1 7.1 5.0 140.6 2253
B 04-327-05 4620 31.9 7.3 5.3 137.6 2205
C 04-287 9200 63.4 6.8 4.8 141.0 2260
D 04-376-10 4520 31.2 7.8 5.8 134.2 2151
E 05-217 3690 25.4 12.6 9.7 129.4 2074
F 03-479-03 8850 61.0 6.2 4.4 140.9 2258
G 04-386 5060 34.9 6.4 4.6 140.5 2252
H 04-411 7690 53.0 7.3 5.2 141.1 2261
J 03-464-90 12440 85.8 3.3 2.2 147.6 2365
K 04-227 10860 74.9 6.2 4.2 149.0 2388
L 05-319-01 7480 51.6 6.6 4.7 141.7 2271
M 05-319-02 6890 47.5 4.9 3.6 137.6 2205
N 05-317 5030 34.7 7.9 5.8 137.2 2199
Results from the tests performed by SEM are presented in Tables 11 and 12. Total air content (including all voids) ranges from 3.5% to 23.6%. The variability is probably due to the compaction voids, since total air content (small air bubbles only) is low for all masonry units. Spacing factor (all voids included) ranges from 75 µm to 394 µm. Results indicate that the masonry units used to validate the frost durability index were made from a wide range of concrete mixtures.
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Table 11 – SEM results – air-void system characteristics
ID
Standard ASTM C457 (All voids included)
Modified ASTM C457
(small compaction voids only)
Modified ASTM C457
(small air bubbles
only)
SEM CODE NCMA Job #
Total air content,
%
Specific area, mm-1
Spacing factor,
µm
Total air content,
%
Specific area, mm-1
Spacing factor,
µm
Total air content,
%
A 04-420-02 13.8 14.7 84 11.8 16.2 91 0.2
B 04-327-05 11.9 16.1 130 8.8 16.1 183 0.5
C 04-287 11.0 17.0 104 9.6 16.9 122 0.3
D 04-376-10 17.2 11.2 89 13.7 13.4 97 0.2
E 05-217 23.6 11.6 75 20.1 13.4 80 0.1
F 03-479-03 9.1 17.7 137 8.9 16.3 168 0.3
G 04-386 9.0 22.5 82 7.3 18.3 126 0.9
H 04-411 10.8 21.4 96 10.0 20.3 110 0.8
J 03-464-90 6.2 15.3 193 4.9 12.8 297 1.1
K 04-227 3.5 14.3 394 2.6 12.7 504 0.5
L 05-319-01 14.2 13.6 85 14.1 14.7 89 0.1
M 05-319-02 13.9 11.0 100 11.5 11.4 120 0.3
N 05-317 12.7 23.7 84 12.4 24.1 85 0.9
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Pressure-saturation test results are presented in Table 12. Connected voids range from 8.6% to 18.8%, and non-connected void volumes range from 2.0% to 5.2%. It should be stressed that the results for some of the specimens showed a high degree of variability. The values were not always consistent when measured on the top versus the bottom section of the specimen. These variations have been taken into account in our analysis of neural network performance.
Table 12 – SEM results – pressure-saturation results
ID
SEM
CODE NCMA Job #
Capillary absorption
(mass) %
Connected voids
(volume) %
Non-connected voids
(volume) %
A 04-420-02 5.4 12.2 2.2
B 04-327-05 5.4 11.9 3.9
C 04-287 5.2 11.6 3.4
D 04-376-10 6.6 14.1 4.5
E 05-217 8.9 18.8 5.2
F 03-479-03 5.4 12.1 3.5
G 04-386 5.3 11.9 3.4
H 04-411 5.1 11.6 3.1
J 03-464-90 3.7 8.6 3.7
K 04-227 3.5 8.7 2.7
L 05-319-01 5.8 13.0 3.0
M 05-319-02 6.4 13.9 2.4
N 05-317 6.6 14.5 2.0
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4.2. Frost durability results based on the neural network and discussion Using the neural network developed in December 2005, frost durability resistance of 13 new masonry mixes was predicted. Neural network results are presented in Table 13. Predictive success rate rises to 69%. The correspondence is based on the 1% limit of mass loss after 100 cycles in tap water. Four predictions are erroneous when compared to experimental results. Numbers in bold in Table 13 are the problematic masonry mixes.
Table 13 – Neural network predictions
ID
SEM CODE NCMA Job #
Experimental results (%)
Neural network results (%)
Correspondence between neural
network and experimental results
A 04-420-02 0.2 0.11 Yes
B 04-327-05 2.5 3.15 Yes
C 04-287 Durable1 -0.46 Yes
D 04-376-10 1.8 4.06 Yes
E 05-217 4.0 6.57 Yes
F 03-479-03 0.4 -0.06 Yes
G 04-386 0.1 2.19 No
H 04-411 0.1 -0.14 Yes
J 03-464-90 Durable1 0.09 Yes
K 04-227 1.4 -0.79 No
L 05-319-01 0.1 0.15 Yes
M 05-319-02 1.5 0.88 No
N 05-317 0.1 2.52 No 1 Durability in tap water confirmed by the frost durability of the masonry units in saline solution.
It is not known with certainty why the neural network was unable to predict frost durability for masonry unit K. One indication is the spacing factor presented in Table 11. The spacing factor for mix K is 394 µm (all voids included) which could be one of the reasons why the samples from mix K are not frost durable. Unfortunately, the spacing factor is not a parameter used in the neural network. The neural network seems to be largely influenced by compressive strength. At 7000 psi, unit K is still durable, according to the neural network prediction. The compressive strength then seems to exert greater influence on neural network prediction than the other parameters, which is normal, since it was the
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parameter that produced the least error in our neural network training (see Table 3). A sensitivity analysis on parameter no. 1 (compressive strength) could be performed. In addition, if the mix contains an aggregate that is not frost durable, this might explain why the neural network is unable to predict frost durability. The reason for the errors obtained on mixes G and N is similar to that found for mix K. It appears that, owing to the poor compressive resistance of both mixes, the neural network predicted “not frost durable.” The compressive strengths of units G and N are in the lower range for the mixes used for neural network training. Mixes with comparable compressive strength showed poor frost durability in Task 2. Since compressive strength is the first parameter that gives the least error (see Table 3), this parameter has an important influence on mass loss prediction. On the other hand, although increasing compressive strength by 15% changes predicted mass loss, the mixes are still not durable. The frost durability prediction for unit M is considered satisfactory, since numerical errors sometimes occur and the predicted result is within the range of experimental results. Since the predicted result is close to the limit for mass loss that characterize a frost durable masonry mix (1% after 100 cycles in tap water), it would be best to run an ASTM C1262 test before judging its frost durability. As mentioned above, the neural network cannot predict mass loss with 100% accuracy. In fact, the neural network made an error (see Table 8 - 0.24). It would be better if the neural network response were Frost Durable or Not Frost Durable instead of mass loss (%). To obtain this type of response, the neural network would require limits of mass loss for frost durability. Depending on the neural network predicted mass loss, the following chart for Frost Durability response is proposed: Predicted mass loss after 100 cycles in Tap Water Frost Durability < 0.75 Durable 0.75 < mass loss < 1.25 ASTM C1262 test needs to be run > 1.25 Not Durable Based on the proposed range, Table 13 needed to be changed. New results are presented in Table 14. The new success rate is 10 out of 13, for a 77% success rate, which is satisfactory for a first-generation neural network.
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Table 14 – Neural network predictions
ID
SEM CODE NCMA Job #
Experimental results
Neural network results
Freeze-thaw durability
A 04-420-02 0.2 0.11 Durable
B 04-327-05 2.5 3.15 Not durable
C 04-287 Durable1 -0.46 Durable
D 04-376-10 1.8 4.06 Not durable
E 05-217 4.0 6.57 Not durable
F 03-479-03 0.4 -0.06 Durable
G 04-386 0.1 2.19 Not durable
H 04-411 0.1 -0.14 Durable
J 03-464-90 Durable1 0.09 Durable
K 04-227 1.4 -0.79 Durable
L 05-319-01 0.1 0.15 Durable
M 05-319-02 1.5 0.88 Needs to be tested
according to ASTM C1262
N 05-317 0.1 2.52 Not durable 1 Durability in tap water confirmed by the frost durability of the masonry units in saline solution.
5.0. DISCUSSION AND CONCLUDING REMARKS The revised neural network for tap water was presented. The most influential parameters and parameter combinations were identified: compressive strength, absorption, total air content, and percentage of non-connected voids. These parameters have an influence on the frost durability of the masonry units and masonry unit producers should be aware of all factors affecting these parameters. By producing good masonry units based on these parameters, the frost durability of the masonry mix should always be good given that all constituents are frost durable. Using the revised neural network, mass losses of the 13 new masonry units were predicted. Compared to the experimental results, the new neural network accurately predicted frost durability 9 times out of 13 for a 69% success rate. The current neural network is therefore able to predict the frost durability of masonry concrete. Some variability was found in the
24
S.E.M. test results from one sample to another for the same masonry mix. Although this variability does not explain the error in predicted mass loss, it indicates that variability may be present in the input parameters. Based on the results presented in this report, it is believed that the neural network for frost durability in tap water works satisfactorily. There remains Task 5 – Assessment of the Frost Durability of Existing Masonry Units. This task is similar to Task 4, except instead of 10 masonry sets being selected on performance under ASTM C1262, they would be selected based on performance in the field. This Task will provide information on the success rate of the neural network to predict frost durability in the field, since ASTM C1262 might not duplicate actual field conditions for the masonry units. In order to perform Task 5, certain tests will have to be run to provide pertinent information on the parameters of the selected masonry units. These tests are:
o Air-void characteristics measurements according to ASTM C457 o Pressure-saturation test o ASTM C1262 – Frost durability of precast concrete blocks (to be determined) o Absorption test o Density measurement o Compressive strength
If the mixes studied in the present report all have recorded field performances, they could be used for continued testing. A field examination would be performed, and performance in the field of the masonry units would be classified as frost durable or not. This neural network can be implemented as a PC software or programmed to function on a Web page, although such an undertaking is not part of the scope of work presented in 2000. Further work on the neural network is also possible and recommended in the years to come. This would allow updating the neural network as the masonry industry evolves. With more data, new neural networks could be trained annually and performances compared with the current one. If better performance is observed, changes in the software could readily be made.
IMPROVING THE FROST DURABILITY OF CONCRETE MASONRY PRODUCTS
Final Report
Task 4 – Laboratory Validation of Durability and Assessment of Frost Durability of 13 Existing Masonry Units
SALT WATER
Project SEM00110
Presented to:
Robert D. Thomas National Concrete Masonry Association
2302 Horse Pen Road Herndon, VA 20171-3499
USA
February 2006
ii
LIMITED LIABILITY STATEMENT
THE RESULTS PRESENTED HERE ARE PRELIMINARY AND UNREVIEWED AND SHOULD BE TREATED AS SUCH. THESE DATA ARE PROVIDED FOR INFORMATION PURPOSES ONLY AND ARE PRESENTLY BEING REVIEWED AND ANALYZED BY S.E.M. EXPERTS FOR PUBLICATION IN A FINAL REPORT. S.E.M. ASSUMES NO RESPONSIBILITY OR LIABILITY FOR THE ACCURACY OR RELIABILITY OF THESE DATA. NO INFORMATION OBTAINED FROM THESE DATA SHALL CREATE ANY WARRANTY WHATSOEVER. CLIENT HEREBY ACKNOWLEDGES AND AGREES THAT THE USE OF ANY INFORMATION CONTAINED IN THIS REPORT IS AT CLIENT'S OWN DISCRETION AND RISK.
iii
TABLE OF CONTENTS
Limited Liability Statement................................................................................................................................ ii Table of contents ............................................................................................................................................... iii 1.0. OBJECTIVES AND PROJECT DESCRIPTION ................................................................. 1 2.0. BACKGROUND INFORMATION ........................................................................................ 2 3.0. IMPROVING THE FROST DURABILITY INDEX (TASK 3)........................................... 3
3.1. Theoretical background: the neural network .............................................................................. 3 3.2. Procedure to establish the neural network .................................................................................. 6 3.3. Neural Network for the freeze-thaw test in a saline solution...................................................... 7
3.3.1. Identification of the most influential parameters and parameter combinations .......................... 9 3.3.2 Optimal number of hidden neurons .......................................................................................... 13
4.0. PREDICTION OF FROST DURABILITY OF 13 NEW MASONRY UNITS ................. 16 4.1. Mixture properties .................................................................................................................... 16 4.2. Frost durability results based on the neural network developed in December 2005 (parameter
nos. 6, 16, and 7) ...................................................................................................................... 20 5.0 CONCLUDING REMARKS ................................................................................................. 22
1.0. OBJECTIVES AND PROJECT DESCRIPTION The main objective of this project was to develop a predictive index for the frost durability of Segmental Retaining Wall Units (SRW units). SWR units are manufactured concrete masonry units used to build earth retaining walls. Frost durability is one of many properties that are important to manufacturers and users of SRW units. The frost durability index would be determined with the help of a neural network which uses a number of physical properties of the SRW units as inputs. The index could be used by masonry producers to improve the durability of their products. Development of this index could also help establish acceptable limits for the degradation of masonry units tested in tap water and saline solution. The study is divided in six different tasks: • Task 1 - Literature Survey • Task 2 - Data Analysis and Additional Testing • Task 3 - Development of a Frost Durability Index • Task 4 - Laboratory Validation of the Frost Durability Index • Task 5 - Assessment of the Frost Durability of Existing Masonry Units • Task 6 - Preparation of the Final Report. The first five tasks address specific aspects of the investigated problem. The project was designed to optimize the use of existing data on the frost durability of masonry units generated by the National Concrete Masonry Association (NCMA). Part one of the study, a comprehensive search of the scientific and technical literature, was conducted in 2001. The Task 1 Final Report was submitted to NCMA on August 7, 2001. Part two, data analysis and additional testing, was conducted in 2003. The Task 2 Final Report was submitted to NCMA on January 31, 2004. Part three, developing a classification tool for durable and non-durable concrete using a neural network, was submitted to NCMA in February 2004. Following a review of the Task 3 Report, NCMA representatives requested some modifications to the proposed neural networks. This report presents the improvements to the neural networks developed in Task 3 for saline solution only. The main requested modifications to the neural network were to ignore certain parameters that are either not easily tracked by the producer (e.g., absolute water content of the concrete mix), not easily discernable from examining the finished product (e.g., cement content, use of admixtures), or not inherent to the concrete matrix (e.g., concrete density).
2
In the latter case, concrete density is not an inherent property because it is dependent on the density of the aggregates used (i.e., lightweight aggregates will yield a lower density product than normal weight aggregates even if the void structure in the concrete matrix is exactly the same). On the other hand, the percentage of non-connected voids, for example, is an inherent property because it describes the concrete matrix and is not dependent on constituent properties. Therefore, it is a parameter that can be used by the neural networks. The neural networks were therefore trained with parameters such as density, cement content, water content, and the use of admixtures ignored. The complete list of ignored parameters is shown in Table 1. This report begins with a presentation of the revised neural network for the saline solution. The results on Task 4 – Validation of the Frost Durability Index are then presented. NCMA provided 13 new sets of SRW unit specimens for frost durability prediction using the revised neural network. Along with providing the specimens, NCMA determined the physical properties of the SRW units including compressive strength, absorption and density. NCMA also conducted the ASTM C 1262 freeze/thaw testing on the 13 sets of specimens and provided the results which were subsequently compared with the predicted results from the neural network to determine the accuracy of the neural network predictions. 2.0. BACKGROUND INFORMATION Initially, this study was to be primarily based on a series of readily available parameters provided by a previous study conducted by NCMA in 1998-19991. Thirty sets of concrete masonry units were manufactured and tested to determine the effects of various production parameters on freeze-thaw durability. Test variables included cement type (Type I and Type III), mixture water content, use of various chemical admixtures (water repellent, plasticizer), aggregate gradation, and compaction time. Compressive strength, absorption, and density of all mixtures were determined by NCMA. Frost durability tests were also carried out by NCMA (in tap water and saline solution) according to ASTM C 1262. In 2001, NCMA decided that, rather than using results provided by the 1998-1999 investigation, the study would be based on test results from a completely new set of units. Consequently, 31 different mixtures were manufactured in April 2002 by two different producers. Many test variables were also modified. All changes made to the original research program as well as the new concrete production data were provided in a previous report (see Task 2 Report – Data Analysis and Additional Testing – January 31, 2004).
1 Memorandum (1998), Preliminary Results of NCMA Phase 3 Durability Research. The NCMA Durability Task Group, 23 p.
3
For all 31 concrete mixtures, compressive strength, absorption, and density were determined by NCMA’s Research & Development Laboratory. Frost durability tests were also carried out by NCMA (in tap water and saline solution) according to ASTM C 1262. Results were made available to us in January 2003. Pressure-saturation tests were conducted by SEM in its laboratory to determine the volume of unconnected voids. These results, used in an earlier report (see Task 3 Report – Elaboration of a Frost Durability Index – February, 2004) to produce the initial neural network, were reused to train the revised neural network. 3.0. IMPROVING THE FROST DURABILITY INDEX (TASK 3) In this part of the study, the frost durability index developed in Task 3 was improved. Certain parameters considered in Task 3 were ignored in Task 4, since they are generally unavailable for existing masonry units. These parameters include density, use of ECA, cement content, high water content, and FT enhancer (see Table 1). As a first step, traditional mathematical tools were used to determine the relationship between mass loss and the different parameters studied (compressive strength, absorption, spacing factor, etc.) However, no linear relationship was found between the different parameters and the frost durability results. Consequently, a more sophisticated method to determine non-linear relationships between the different parameters or combinations of parameters and freeze-thaw results was used. This method, known as the Neural Network method, is described below. 3.1. Theoretical background: the neural network The neural network is composed of simple processing entities (called nodes, units, or neurons) operating in parallel. As in the animal brain, the network function is determined largely by the connections between the neurons. A neural network can be trained to perform a particular function by adjusting the strengths (or weights) of these connections. Briefly, neural networks are trained so that a single output value is generated from all the input values applied to the neuron. As shown in Figure 1, batch training of a neural network proceeds by adjusting the weight connections and vectors based on a set of inputs presented to the neuron.
4
Figure 1 – Neural network architecture
Multi-layer networks were used to analyze the freeze-thaw results in saline water with respect to two functions: a non-linear sigmoid function between input data and the hidden layer of neurons, and a linear function between the hidden neurons and the output neuron (see Figure 2). The neural network method was used to identify the most reliable parameters and combinations of parameters for the durability results. The eleven parameters tested are presented in Table 1.
Neural network: neurons and the
weighted unidirectional connections between
them
Input Output Error = difference
between target and actual
output
Target
Weight adjustment
5
Figure 2 – Neural network used in this study
PR
P2
P1
Input Data
Hidden Neurons
Output Data
W11
W21
WRn
b1
b2
bn-1
bn
First layer output is calculated by sigmoid function.
Second layer output is calculated by linear function.
6
Table 1 – Parameters and measurement units
Parameter tested Unit
1 - Compressive strength psi
2 - Absorption pcf
3 - Density pcf
4- Total air content considering all voids %
5- Spacing factor considering all voids µm
6- Total air content considering small compaction voids only %
7- Spacing factor considering small compaction voids only µm
8- Total air content considering small air bubbles only %
9 - Cement content %
10 - Capillary absorption %
11 - Connected voids %
12 - Non-connected voids %
13 - Use of ECA 1 if used or 0 if not
14 - Use of high water content 1 if used or 0 if not
15 - Use of FT Enhancer 1 if used or 0 if not
16 - Use of no, low, or high dosage of air-entraining admixture 0 if no air, 1 if low air, 2 if high air Note: Crossed-out parameters were ignored in the development of the revised neural network. 3.2. Procedure to establish the neural network The neural network development followed the same procedure as the one used for tap water, comprising the following six steps: 1. A neural network was formed and trained with the input data (parameter values
presented in Table 1 and percent mass loss results for 24 of the 31 samples). 2. For each input parameter, the network predicted durability results for the 6 test samples
(in bold in Table 2, page 7). The 6 samples were taken from the list of 31 samples presented in our Task 2 Report – Data Analysis and Additional Testing, dated January 31, 2004).
3. Numerical results were compared with the experimental results obtained on the
6 samples. Simulation errors were calculated. 4. The parameter that produced the minimum error was retained.
7
5. A second simulation was then performed to arrive at a combination of the parameter
retained in step 4 and the remaining series of parameters to determine if there was a second parameter that, when combined with the original retained parameter, generated an error lower than that obtained in step 4. The parameter that produced the minimum error was then retained along with the original retained parameter from step 4.
6. The process was then repeated with the two retained parameters from step 5 being
combined with the remained parameters and the errors resulting from each combination being calculated to determine of there was a minimum error that was lower than the error obtained in step 5. If there was a parameter that fit this criterion, then it was retained and the process was continually repeated until the error obtained was equal to or higher than the error the previous step. The process was then stopped.
7. Once the more reliable parameters had been retained from the process described in
steps 1 to 6, the number of hidden neurons was increased, varying from 2 to 10. Error was calculated for each possibility and the number of hidden neurons yielding the minimum error was retained.
3.3. Neural Network for the freeze-thaw test in a saline solution Eleven parameters were used as input data to determine the frost durability index for the specimens tested in saline solution (see Table 1). The output data represent percent mass loss in a saline solution after 40 cycles. As shown in Table 2, our neural network was formed and trained using results on 24 samples (concrete mixtures). Six samples were retained to test the accuracy of our neural network. One sample (10-B-ECA-NA-FT) was discarded from the study owing to a substantial amount of missing data. The 24 samples used to train the network and the 6 samples (in red/bold) used to test the retained neural network are presented in Table 2.
8
Table 2 – Specimens used for network training and for model validation (Tests in saline solution)
ID (SET-A) 1 Freeze-Thaw Durability1 ID (SET-B) Freeze-Thaw Durability1
1- 13-A-Ref Not durable 17- 13-B-Ref Not durable
2- 10-A-HW Durable 18- 10-B-HW Durable
3- 13-A-HW Durable 19- 13-B-HW Durable
4- 16-A-HW Durable 20- 16-B-HW Durable
5- 10-A-ECA-NA Not durable 21- 10-B-ECA-NA Not durable
6- 13-A-ECA-NA Not durable 22- 13-B-ECA-NA Not durable
7- 16-A-ECA-NA Not durable 23- 16-B-ECA-NA Not durable
8- 10-A-ECA-NA-FT Not durable 24- 10-B-ECA-NA-FT 2 Not durable
9- 13-A-ECA-NA-FT Not durable 25- 13-B-ECA-NA-FT Not durable
10- 16-A-ECA-NA-FT Not durable 26- 16-B-ECA-NA-FT Not durable
11- 10-A-ECA-LA-FT Not durable 27- 10-B-ECA-LA-FT Not durable
12- 13-A-ECA-LA-FT Not durable 28- 13-B-ECA-LA-FT Not durable
13- 16-A-ECA-LA-FT Durable 29- 16-B-ECA-LA-FT Durable
14- 13-A-ECA-HA-FT Durable 30- 13-B-ECA-HA-FT Durable
15- 16-A-ECA-HA-FT Not durable 31- 16-B-ECA-HA-FT Durable
16- 16-A-2xECA-NA-FT Not durable 1 Samples in red/bold were used to test the neural network previously trained with the 24 other sample results. 2 Sample 24 (10-B-ECA-NA-FT) was not considered in this study. In the earlier Task 2 Report, certain air-void system characteristics were not determined for this sample due to high void content (Table 7 of Task 2 Report).
9
3.3.1. Identification of the most influential parameters and parameter combinations Following the initial test series combining all parameters, the neural network determined that parameter no. 6 presents the lowest error (probably the most reliable parameter for durability results) (see Table 3). This parameter corresponds to total air content considering small compaction voids only. Albeit the minimum error in our study, it nevertheless represents a substantial error, indicating that the neural network with the allowed parameters might be unable to accurately predict mass loss in this test. A second series of calculations was completed to determine the combination of this parameter and another parameter that would produce lower error.
Table 3 – Test using all parameters (step one)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 6.62 2 - Absorption (pcf) 6.11 3 - Density (pcf) 4 - Total air content considering all voids (%) 5.30 5 - Spacing factor considering all voids (µm) 7.35
6 - Total air content considering small compaction voids only (%) 4.29 7 - Spacing factor considering small compaction voids only (µm) 7.35 8 - Total air content considering small air bubbles only (%) 5.10 9 - Cement content (%) 10 - Capillary absorption (%) 7.07 11 - Connected voids (%) 7.30 12 - Non-connected voids (%) 6.61 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
10
Results presented in Table 4 show that the second parameter, which corresponds to the use of no, low, or high dosage of air-entraining admixture (parameter no. 16), in combination with parameter no. 6 (total air content considering small compaction voids only) yields the lowest error (2.78). Because this error is lower than the previous one (2.78 < 4.29), the two parameters combined are more useful than either parameter independently. Parameter no. 16 was retained, even though it was originally meant to be ignored, since it gives a significantly lower error than the other combinations. In the continuation, parameter nos. 6 and 16 were retained, and a further calculation series was completed to determine the best choice for the third combined parameter.
Table 4 – Test using first retained parameter (total air content, considering small compaction voids only) and remaining parameters (step two)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 4.16
2 - Absorption (pcf) 4.29
3 - Density (pcf)
4 - Total air content considering all voids (%) 4.32
5 - Spacing factor considering all voids (µm) 4.01
6 - Total air content considering small compaction voids only (%)
7 - Spacing factor considering small compaction voids only (µm) 4.25
8 - Total air content considering small air bubbles only (%) 4.31
9 - Cement content (%)
10 - Capillary absorption (%) 4.94
11 - Connected voids (%) 4.88
12 - Non-connected voids (%) 4.34
13 - Use of ECA
14 - Use of high water content
15 - Use of FT enhancer
16 - Use of no, low, or high dosage of air-entraining admixture
2.78
11
Table 5 clearly shows that the combination of parameter no. 6 (total air content considering small compaction voids only), parameter no. 16 (use of no, low, or high dosage of air-entraining admixture), and parameter no. 7 (spacing factor considering small compaction voids only) is very useful. The error generated by the combination of these three parameters is 2.63, lower than that generated by the combination of total air content considering small compaction voids only and the use of no, low, or high dosage of air-entraining admixture (2.78 – see Table 4). The combination of parameter nos. 6 and 16 with parameter no. 7 is therefore useful. It should be emphasized that when parameter no. 14 (use of high water content) was ignored, the neural network generated a higher error (2.63 compared to 1.92). Nonetheless, parameter no. 14 was ignored since, from discussion with NCMA representatives, this information is not normally gathered by concrete producers. It was evaluated for the masonry mixes produced in Task 2 because the mixes were produced by only 2 producers. It was therefore decided that parameter no. 14 would be ignored for the training of the new 2005 neural network.
Table 5 – Test using two retained parameters (total air content, considering small compaction voids only and use of no, low or high dosage of air-entraining admixture)
and remaining parameters (step three)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 2.87 2 - Absorption (pcf) 2.79 3 - Density (pcf) 4 - Total air content considering all voids (%) 3.10 5 - Spacing factor considering all voids (µm) 2.91 6 - Total air content considering small compaction voids only (%) 7 - Spacing factor, considering small compaction voids only (µm) 2.63 8 - Total air content considering small air bubbles only (%) 2.71 9 - Cement content (%) 10 - Capillary absorption (%) 3.19 11 - Connected voids (%) 3.27 12 - Non-connected voids (%) 2.98 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
12
From the results presented in Table 6, a threshold of optimization was reached. The error generated by the addition of one more parameter is higher. Since the neural network could not be further improved, it was decided to establish the first three parameters retained (total air content, considering small compaction voids only and use of no, low, or high dosage of air-entraining admixture) as the optimal set. This new neural network produces a higher error than that generated in a previous analysis (2.63 compared to 0.74). Table 6 – Test using four retained parameters (compressive strength, absorption, total
air content, and non-connected voids) and remaining parameters (fourth step)
Parameter retained Root mean square error (verification)
1 - Compressive strength (psi) 2.84 2 - Absorption (pcf) 2.73 3 - Density (pcf) 4 - Total air content considering all voids (%) 3.08 5 - Spacing factor considering all voids (µm) 2.66 6 - Total air content considering small compaction voids only (%) 7 - Spacing factor considering small compaction voids only (µm) 8 - Total air content considering small air bubbles only (%) 2.91 9 - Cement content (%) 10 - Capillary absorption (%) 3.25 11 - Connected voids (%) 3.25 12 - Non-connected voids (%) 3.05 13 - Use of ECA 14 - Use of high water content 15 - Use of FT enhancer 16 - Use of no, low, or high dosage of air-entraining admixture
13
3.3.2 Optimal number of hidden neurons To optimize the neural network based on the three retained parameters, the number of hidden neurons was increased (see Figure 2), varying from 2 to 10. The results were compared with the results of the 6 samples used to determine the accuracy of the model (errors were calculated for each possibility and the number of hidden neurons yielding the smallest error was retained (see Table 7)).
Table 7 – Results analysis of neural network based on parameter nos. 6, 16, and 7
Number of neuron layers Error 2 2.63 3 2.63 4 2.63 5 2.76 6 2.63 7 2.65 8 2.80 9 2.89
10 3.22 The neural network formed with 3 hidden neurons was retained, although a higher number of neurons yielded an equal error of 2.63. Two significant reasons justify the choice of a lower number of hidden neurons: 1. The higher the number of hidden neurons, the higher the number of weights connecting
the input parameters to the hidden neurons. Better network performance is obtained when the relationship between the number of hidden neurons and the number of observations (experimental results) is minimal.
2. Non-linearity increases with the increase in number of hidden neurons, rendering the
network less realistic. Results presented in the Task 2 Report are quite variable, even for 5 specimens made from the same concrete mixture. Increasing the number of neurons would probably result in overtraining of the network and potentially unrealistic results. A greater number of hidden neurons in a network do not necessarily mean a better network.
The high error obtained by the new neural network indicates that the actual neural network might be unable to accurately predict frost durability in saline solution with the parameters used for training.
14
3.3.3 Statistical analysis of the results given by the neural network optimized with two hidden neurons and based on parameter nos. 6, 16, and 7 (respectively, total air content considering small compaction voids only, use of no, low, or high dosage of air-entraining admixture, and spacing factor considering small compaction voids only) Once the neural network was optimized, experimental mass loss results were compared with the numerical results calculated by the neural network. As described above, certain experimental results were used for network training (24 specimens, Table 8, column 2). The network was subsequently tested for the six samples not used for network training. The results, presented in Table 8, show that mass loss results calculated by the neural network reasonably approximate experimental results on the 24 samples at 71%. The new neural network has a lower success rate than that presented in 2004 (79% success rate in 2004). The correspondence between neural network calculations and experimental results on the six samples is average, with the neural network duplicating experimental results at 50%. Correspondence in Table 8 is defined by mass loss predicted by the neural network. Correspondence is obtained when both predicted and experimental mass losses indicate frost durability based on the 1% limit of mass loss. The average success of this new neural network is due in part to critical parameters that are currently either not known or not considered, for example, density. In 2004, using the density parameter, an error of 0.74 was obtained, which is lower than that obtained by the neural network presented here. This suggests that density is a very important parameter for the prediction of frost durability of masonry units in saline solution.
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Table 7 – Experimental versus numerical results for all specimens
Specimen identification
Experimental results used for network
training 1
Neural Network results
Correspondence between neural and
experimental results
Experimental results used for network testing
Neural Network results
Correspondence between neural and
experimental results
(%) (%) (%) (%) 1 1.26 13.64 Yes 2 0.41 10.88 No 3 0.12 2.88 No 4 0.49 5.19 No 5 37.28 12.72 Yes 6 6.00 8.19 Yes 7 12.68 9.14 Yes 8 59.2 11.52 Yes 9 5.00 9.74 Yes
10 8.46 5.53 Yes 11 1.28 3.56 Yes 12 0.23 -0.17 Yes 13 0.49 1.56 No 14 0.65 -0.20 Yes 15 2.55 7.33 Yes 16 3.64 12.15 Yes 17 3.24 12.53 Yes 18 0.98 6.53 No 19 0.54 2.58 No 20 0.34 5.16 No 21 1.73 9.50 Yes 22 15.60 8.72 Yes 23 2.44 8.27 Yes 24 25 14.27 13.32 Yes 26 1.35 14.82 Yes 27 57.36 10.29 Yes 28 4.53 4.71 Yes 29 0.87 2.15 No 30 1.00 0.73 No 31 0.30 1.92 No
Statistics (% success) 71 Statistics (% success) 50
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4.0. PREDICTION OF FROST DURABILITY OF 13 NEW MASONRY UNITS 4.1. Mixture properties Once the neural network was reviewed and improved (see earlier section), NCMA sent to our laboratories a new set of 13 masonry concrete mixes. Technical data on these mixes were provided by NCMA, including compressive strength, absorption, and density. Other properties were determined at our laboratories with supplementary testing, as follows: ♦ Determination of the air void characteristics – ASTM C457 ♦ Pressure-saturation test. Results of the tests performed by NCMA (see Table 10) indicate that the 13 new masonry concrete mixes were made from a wide range of concrete mixtures. Compressive strength ranged from 3690 psi to 12440 psi, while absorption ranged from 3.3 pcf to 12.6 pcf. Density of the masonry units ranged from 129.4 pcf to 149.0 pcf. These masonry samples provided a good evaluation of the neural network to predict the frost durability of masonry mixes.
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Table 9 – NCMA results
ID Compressive Strength Absorption Density
SEM CODE NCMA Job # (psi) (MPa) (pcf) (%) (pcf) (kg/m3)
A 04-420-02 6970 48.1 7.1 5.0 140.6 2253
B 04-327-05 4620 31.9 7.3 5.3 137.6 2205
C 04-287 9200 63.4 6.8 4.8 141.0 2260
D 04-376-10 4520 31.2 7.8 5.8 134.2 2151
E 05-217 3690 25.4 12.6 9.7 129.4 2074
F 03-479-03 8850 61.0 6.2 4.4 140.9 2258
G 04-386 5060 34.9 6.4 4.6 140.5 2252
H 04-411 7690 53.0 7.3 5.2 141.1 2261
J 03-464-90 12440 85.8 3.3 2.2 147.6 2365
K 04-227 10860 74.9 6.2 4.2 149.0 2388
L 05-319-01 7480 51.6 6.6 4.7 141.7 2271
M 05-319-02 6890 47.5 4.9 3.6 137.6 2205
N 05-317 5030 34.7 7.9 5.8 137.2 2199
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Results from the tests performed at our laboratories are presented in Tables 10 and 11. Total air content (including all voids) ranges from 3.5% to 23.6%. The variability is probably due to the compaction voids present in each concrete mixes, since total air content (small air bubbles only) is low for all masonry units. Spacing factor (all voids included) ranges from 75 µm to 394 µm. Results indicate that the masonry units used to validate the frost durability index were made from a wide range of concrete mixtures.
Table 10 – SEM results – air-void system characteristics
ID
Standard ASTM C457 (All voids included)
Modified ASTM C457
(small compaction voids only)
Modified ASTM C457
(small air bubbles
only)
SEM CODE NCMA Job #
Total air content, %
Specific area, mm-1
Spacing factor,
µm
Total air content, %
Specific area, mm-1
Spacing factor,
µm
Total air content, %
A 04-420-02 13.8 14.7 84 11.8 16.2 91 0.2
B 04-327-05 11.9 16.1 130 8.8 16.1 183 0.5
C 04-287 11.0 17.0 104 9.6 16.9 122 0.3
D 04-376-10 17.2 11.2 89 13.7 13.4 97 0.2
E 05-217 23.6 11.6 75 20.1 13.4 80 0.1
F 03-479-03 9.1 17.7 137 8.9 16.3 168 0.3
G 04-386 9.0 22.5 82 7.3 18.3 126 0.9
H 04-411 10.8 21.4 96 10.0 20.3 110 0.8
J 03-464-90 6.2 15.3 193 4.9 12.8 297 1.1
K 04-227 3.5 14.3 394 2.6 12.7 504 0.5
L 05-319-01 14.2 13.6 85 14.1 14.7 89 0.1
M 05-319-02 13.9 11.0 100 11.5 11.4 120 0.3
N 05-317 12.7 23.7 84 12.4 24.1 85 0.9
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Pressure-saturation test results are presented in Table 11. Connected voids range from 8.6% to 18.8%, and non-connected void volumes range from 2.0% to 5.2%. It should be stressed that the results show variability. The values were not always consistent when measured on the top versus the bottom section. These variations will be taken into account in the analysis of the neural network versus the results of NCMA’s ASTM C 1262 test. This variability might explain some errors in the neural network predictions.
Table 11 – SEM results – pressure-saturation results
ID
SEM
CODE NCMA Job #
Capillary absorption
(mass) %
Connected voids
(volume) %
Non-connected voids
(volume) %
A 04-420-02 5.4 12.2 2.2
B 04-327-05 5.4 11.9 3.9
C 04-287 5.2 11.6 3.4
D 04-376-10 6.6 14.1 4.5
E 05-217 8.9 18.8 5.2
F 03-479-03 5.4 12.1 3.5
G 04-386 5.3 11.9 3.4
H 04-411 5.1 11.6 3.1
J 03-464-90 3.7 8.6 3.7
K 04-227 3.5 8.7 2.7
L 05-319-01 5.8 13.0 3.0
M 05-319-02 6.4 13.9 2.4
N 05-317 6.6 14.5 2.0
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4.2. Frost durability results based on the neural network developed in December 2005 (parameter nos. 6, 16, and 7)
Using the neural network developed in December 2005 and presented in this report, the frost durability resistance of the 13 new masonry mixes was evaluated. Neural network results are presented in Table 12. From the results, the new model predicts frost durability for only 2 of the 13 masonry mixes when tested in saline solution. The frost durable masonry mixes are J and K (NCMA Id: 03-464-90 and 04-227). The correspondence (which is based on the 1% limit on the mass loss after 40 cycles) between the neural network and the experimental results is average. This neural network has a success rate of 54% (7 out of 13). For prediction purposes, it was considered that all new masonry mixes contained a low dosage of air-entraining admixture. If in fact they did not, new results will be presented once the information becomes available.
Table 12 – Neural network prediction (Neural network developed in 2005)
ID
SEM CODE NCMA Job #
Experimental results 40 cycles/50 cycles
Neural network results
Correspondence between neural
network and experimental results
A 04-420-02 0.4 11.64 No
B 04-327-05 3.0 5.81 Yes
C 04-287 0.4 8.21 No
D 04-376-10 4.2 13.29 Yes
E 05-217 1.4 15.45 Yes
F 03-479-03 2.3 6.25 Yes
G 04-386 2.6 1.45 Yes
H 04-411 0.3 4.82 No
J 03-464-90 0.2 -0.89 Yes
K 04-227 Not durable based on
tap water results -1.26 No
L 05-319-01 0.3 13.64 No
M 05-319-02 5.0 10.86 Yes
N 05-317 0.1 8.86 No
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4.3. Frost durability results based on the 2004 neural network (parameter nos. 1, 11, 12, and 15) Since the neural network developed in February 2004 gave a lower error, it was used to predict mass loss for the new sets of masonry mixes (see Report Task 3 – Study on Improving the Frost Durability of Concrete Masonry Products, submitted February 2004). Results given by the 2004 model are presented in Table 13. From the results, the 2004 model predicts that only 2 of the 13 masonry mixes would be frost durable when tested in saline solution. Predicted durability results do not correspond to predictions by the new model. Based on the results of the 2004 neural network, the frost durable mixes are C and F (NCMA Id: 04-287 and 03-479-03). This neural network has a similar success rate than the other neural network presented in this report. The prediction of the frost durability, based on the limit of 1% after 40 cycles, is good on 7 out of 13 masonry mixes. For prediction purposes, it was considered that all new masonry mixes contained no Freeze-Thaw Enhancer. If in fact some mixes contained Freeze-Thaw Enhancer, new results will be presented once that information becomes available.
Table 13 – Neural network prediction (Neural network of 2004)
ID
SEM CODE NCMA Job #
Experimental results Neural network results
Correspondence between neural
network and experimental results
A 04-420-02 0.4 6.30 No
B 04-327-05 3.0 24.76 Yes
C 04-287 0.4 0.00 Yes
D 04-376-10 4.2 27.49 Yes
E 05-217 1.4 44.79 Yes
F 03-479-03 2.4 -0.18 No
G 04-386 2.6 20.60 Yes
H 04-411 0.3 1.92 No
J 03-464-90 0.1 1.44 No
K 04-227 Not durable based on
tap water results 1.94 Yes
L 05-319-01 0.3 2.52 No
M 05-319-02 5.0 7.06 Yes
N 05-317 0.1 33.15 No
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5.0 CONCLUDING REMARKS In this report, the revised neural network for saline solution was presented. In 2004, it was observed that it was difficult to find correlation between the mass loss results and the parameters which were thought to be the most significant. Again in 2005, the same observations were made. The most influential parameters and parameter combinations, in our 2005 analysis, were identified: total air content considering small compaction voids only, use of no, low, or high dosage of air-entraining admixture, and spacing factor considering small compaction voids only. The parameter related to the use of air-entraining admixture (no, low or high) was kept due to its beneficial effect on the prediction of the neural network. Using the new neural network, mass losses of the 13 new masonry units were predicted. The first neural network developed in Task 3 (submitted in February 2004) was also used to predict mass losses in the 13 new masonry units, due to the lower success rate (measured on the results of Task 2) of the new neural network. Both neural networks indicated frost durability for only 2 masonry mixes, and frost durability predictions differed between the two neural networks. It is noteworthy that both neural networks predicted very poor performance for mixes D and E (NCMA Id: 04-376-10 and 05-217). These two mixes have very high total air content with almost no air bubble (spherical air void). This most probably indicates poor compaction of the units. With the prediction results and the experimental results, it is observed that both neural networks have the same success rate (7 good predictions out of 13 masonry mixes). This is a 54% success rate which is quite similar to the success rate on the 6 retained mixes used for validation in Task 3 (see Table 7). In 2005, we had developed a third neural network which use all parameters presented in Table 1. The selected parameters were: Parameter no. 6. Total air content considering small compaction voids only Parameter no. 16. Use of no, low, or high dosage of air-entraining admixture Parameter no. 14. Use of high water content Parameter no. 12. Non-connected voids Parameter no. 11. Connected voids This neural network indicates the most influential parameters for frost durability of masonry mixes used in Task 2. Using these 5 parameters and 4 hidden neurons, the neural network gave an error of 0.80. Due to lack of information on parameters nos. 14 and 16, the performance of this neural network could not be evaluated and it is not sure it would have a
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higher success rate. In the event that the required information could be readily obtained, this neural network could be tested easily. The poor performance of the presented neural networks might be associated with the fact that, for the 30 mixes used for training, 9 results were approximated since the samples failed before the completion of 40 cycles (e.g., 25 or 30 cycles). Using marginal concrete for training a neural network can lead to poor performance of the neural network. Larger numbers of samples and results would likely improve neural network performance. Using another series of masonry mixes, that would be more representatives of what it used in the industry, might trained a better neural network. It is believed that at this stage, the developed tool is good for classification of durable and non-durable concrete but still requires improvement. This improvement might require more experimental testing to produce a bigger, better and more objective database.
