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NEW MEXICO DEPARTMENT OF TRANSPORTATION
PERMEABILITY AND MOISTURE DAMAGE CHARACTERISTICS OF ASPHALT PAVEMENTS
PRESENTED BY RAFI TAREFDER
MOHIUDDIN AHMAD DEPARTMENT OF CIVIL ENGINEERING
OVERVIEW
Introduction – Problem statement and objectives
Objective-1
Objective-2
Objective-3
Objective -4
Objective -5
Conclusion and recommendation
INTRODUCTION
In recent time, most of the pavements are constructed using
SuperPave mix design method
Superpave uses coarser aggregates and increase the amount
of interconnected voids.
Increase of interconnected voids may increase permeability.
The permeability of New Mexico pavements and mixes are
not known.
Attempts are made to determine the permeability of New
Mexico Pavements and mixes
Permeability of New Mexico Mixes
INTRODUCTION
Permeability specifications are developed based on permeability-total pores correlation
Total pores are easy to determine however it contains
isolated and dead-end pores which has no contribution to flow
No efforts were made to determine permeable pores of
asphalt pavements
Permeable pores
Isolated pores
Dead end pores
Effective pores
Permeability and Permeable Pores
INTRODUCTION
Permeability determined in the field is affected by several
factors
Field measurement doesn’t gives exact permeability of a
layer or mix
Laboratory permeability give 1D permeability only
although field permeability is 3D
Surface course
Tack coat
Binder course
Prime coat
Seal coat/Fog seal/OGFC
Field and Laboratory Permeability Base or Subgrade
INTRODUCTION
Permeability and Moisture Damage
34 out of 50 states (Hicks 1991) or 15 out of 24 states
surveyed by Mogawer et al. (2002) have some pavements
that suffer from moisture damage
Reducing permeability may reduce interaction between
water and pavement materials
INTRODUCTION
Permeability Specification
Four US States have their own permeability specification The maximum and minimum permeability specification
values are 125E-5 cm/s and 0 cm/s respectively
Permeability specification for New Mexico pavements are not known
OBJECTIVES
The main objective of this study is to find a permeability specification for New Mexico mixes. The specific objectives are: Develop a database of permeability of New Mexico
pavements and mixes and develop a permeability specification for New Mexico.
Determine the correlation of permeability with permeable pore, effective pores and dead end pores and mix gradation.
Determine correlation between field and laboratory permeability.
Find whether permeability is related to moisture damage or not.
TESTING METHODOLOGY
Survey: Selection of Candidate Pavements
Field Permeability Test and Coring
Laboratory Testing
Laboratory Permeability Testing on Full Depth Cored Samples Samples Separated into Layers
Laboratory Tests for Permeability
Dry Conditioning
AASHTO T 283 MIST
Good Performing Pavements Bad Performing Pavements
IDT
Moist Conditioning
Indirect Tensile Strength (IDT)
SURVEY AND SELECTION OF CANDIDATE PAVEMENTS
Based on i) Field engineers and supervisors experience ii) Visual inspection of stripping of cores at interface The flowing pavements are selected
Good Performing Pavements (Not showing Moisture Damage)
Bad Performing pavements (Showing Moisture Damage)
Pavement location ID Number of layers
Pavement location ID Number of layers
US285 MP126.3 1 2 US70 MP289.26 9 3 US285 MP285.25 2 3 US70 MP282.2 10 3 NM344 MP1.80 3 1 US70 MP272.67 11 3 NM14 MP46.8 4 3 NM264 MP10 12 1 US491 MP60.7 5 3 US491 MP60.9 13 3 US491 MP60.5 6 3 US285 MP140.53 14 2 NM14 MP46.9 7 3 NM344 MP1.82 15 1 NM344 MP1.84 8 2 I40 MP23.1 16 3
TEST MATRIX
6 field permeability tests on each location
Total field permeability tests = 16 × 6 = 96
Permeability test of full depth cores = 16 × 3 = 48
Total different layers or mixes = (10 × 3 + 3 × 2 + 3 × 1) =
39
Number of laboratory permeability tests on = 39 × 9 = 351
Dry conditioning = 39 × 3 =117
AASHTO T 283 conditioning = 39 × 3 = 117
MIST conditioning = 39 × 3 = 117
MATERIALS DATA The mix design data of field collected cores are determined in the laboratory and shown below:
Pavement Sections Gmb Gmm VA AC Pavement Sections Gmb Gmm VA AC
US285 MP 126.3TL 2.37 2.44 3.07 4.54 US70 MP272.67ML 2.22 2.45 9.25 4.04
US285 MP 126.3BL 2.19 2.37 7.60 4.93 US70 MP272.67BL 2.33 2.41 3.33 3.31
US285 MP285.25TL 2.41 2.58 6.39 6.13 NM264 MP10TL 2.32 2.47 6.05 4.61
US285 MP285.25ML 2.43 2.58 5.78 5.80 US285 MP140.53TL 2.27 2.47 8.10 6.40
US285 MP285.25BL 2.41 2.52 4.18 6.05 NM344 MP1.82TL 2.26 2.50 9.77 5.93
NM344 MP1.80TL 2.28 2.51 9.03 5.30 US285 MP152TL 2.25 2.45 7.88 5.94
NM14 MP46.8NBTL 2.28 2.49 8.30 5.60 US285 MP152ML 2.22 2.43 8.77
NM14 MP46.8NBML 2.33 2.44 4.42 5.50 US285 MP152BL 2.20 2.45 10.13 4.64
NM14 MP46.8NBBL 2.32 2.47 6.33 5.30 US491 MP60.5TL 2.21 2.47 10.36 5.42
US491 MP60.7TL 2.34 2.47 5.19 5.60 US491 MP60.5ML 2.31 2.44 5.29 5.50
US491 MP60.7ML 2.33 2.48 6.27 5.00 US491 MP60.5BL 2.31 2.44 5.29 5.51
US491 MP60.7BL 2.33 2.47 5.85 5.80 US491 MP60.9TL 2.33 2.46 5.35 5.51
NM344 MP1.84TL 2.31 2.46 6.10 4.63 US491 MP60.9ML 2.33 2.47 5.61 5.44
NM344 MP1.84BL 2.35 2.46 4.65 6.07 US491 MP60.9BL 2.34 2.44 4.02 5.63
US70 MP289.26TL 2.28 2.45 7.13 6.60 NM14 MP46.8SBTL 2.30 2.435 5.71 6.62
US70 MP289.26ML 2.23 2.46 9.29 6.99 NM14 MP46.8SBML 2.33 2.51 7.17 4.56
US70 MP289.26BL 2.22 2.45 9.23 7.13 NM14 MP46.8SBBL 2.29 2.47 7.39 5.79
US70 MP282.2TL 2.24 2.45 8.59 4.13 NM14 MP46.9SBTL 2.16 2.51 13.70 2.68
US70 MP282.2ML 2.21 2.46 10.30 4.10 NM14 MP46.9SBML 2.33 2.44 4.51 5.49
US70 MP282.2BL 2.29 2.43 5.97 4.52 NM14 MP46.9SBBL 2.33 2.46 5.12 6.55
US70 MP272.67TL 2.24 2.45 8.64 4.72 US285 MP140.53BL 2.08 2.39 12.97 5.67
GRADATION CHART FOR GOOD PAVEMENTS
125 9.5 4.75 2 0.425 0.075 0
102030405060708090
100
Perc
ent p
assi
ng
Sieve size raised to 0.45 power
Max density lineUS285MP126.3BLUS285MP140.53BLUS285MP152TLUS491MP60.9MLUS491MP60.9BL
19
12.5 mm NMAS
12.5 9.5 4.75 2 0.425 0.075 0
10
20
30
40
50
60
70
80
90
100
Perc
ent p
assi
ng
Sieve size raised to 0.45 power
Max density line US285MP126.3TL
US285MP140.53TL US285MP285.25TL
US285MP285.25ML US285MP285.25BL
US491MP60.9TL US491MP60.5TL
US491MP60.5ML US491MP60.5BL
US491MP60.7TL US491MP60.7ML
US491MP60.7BL NM14MP46.8TL
NM14MP46.8ML NM14MP46.8BL
NM14MP46.9TL NM14MP46.9ML
NM14MP46.9BL
19 mm NMAS
GRADATION CHART FOR GOOD PAVEMENTS
12.5 9.5 4.75 2 0.425 0.075 0
10
20
30
40
50
60
70
80
90
100
Perc
ent p
assi
ng
Sieve size raised to 0.45 power 12.5 mm NMAS
Max density line US264MP10TL
19
12.5 9.5 4.75 2 0.425 0.075 0
10
20
30
40
50
60
70
80
90
100
Perc
ent p
assi
ng
Sieve size raised to 0.45 power 19 mm NMAS
Max density line US70MP289.26TL
US70MP289.26ML US70MP289.26BL
US70MP282.2TL US70MP282.2ML
US70MP282.2BL US70MP272.67TL
US70MP272.67ML US70MP272.67BL
NM344MP1.82TL NM344MP1.8TL
NM344MP1.84
25
Permeability Testing
Laboratory Testing Field Testing
Full Length Individual Layers
PERMEABILITY TESTING
PERMEABILITY TESTING
Permeability is the material’s ability to transmit water
Mathematically defined by Darcy’s law: 𝑉 = 𝑘𝑘
Usually determined by falling head or constant head method
For AC, falling head method is widely used.
Permeability of AC is determined by:
𝑘 =𝑎𝑎𝐴𝐴
𝑙𝑙ℎ1ℎ2
× 𝐴𝑐
where 𝐴𝑐 is a correction factor for temperature.
Sample area, A
h1 h2
Tube area, a
Length, L
FIELD PERMEABILITY TESTING
NCAT Field Asphalt Permeameter
Procedure Permeameter base is placed on
the pavement with sealing Dumbbells are placed to protect
leaking and uplift Water is allowed to flow for few
minutes for saturation
Limitations Saturation Flow direction Difficulty of managing traffic and field crew Open Graded Friction Coarse (OGFC), tack coat, prime
coat, seal coat etc. Variable temperature
LABORATORY PERMEABILITY TESTING
Florida Apparatus
Procedure Sample is saturated using
CorelokTM Sample is placed inside the
cylinder A pressurized membrane confines
the side
Advantages Saturation Vertical flow No coating or OGFC Fixed temperature
RESULTS
The field permeability values range from 0 to 1208 × 10-5
cm/s with an average of 178 × 10-5 cm/s Only 5 out of 16 pavements’ permeability are higher than
that limit 125E-5 cm/s
0200400600800
100012001400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Perm
eabi
lity
(x10
-5 c
m/s
)
Pavement sections
Field Permeability Test Results
LABORATORY PERMEABILITY TEST RESULTS: FULL DEPTH SAMPLES
Permeability of almost all samples is almost equal to zero (have average permeability of 3.15 × 10-5 cm/s)
Discontinuity of flow path and presence of different coating at interface retards the flow
05
101520253035404550
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Perm
eabi
lity
(x 1
0-5 c
m/s
)
Pavement sections
LABORATORY PERMEABILITY TEST RESULTS: LAYERED SAMPLES
The average permeability of top layer is 65 × 10-5 cm/s, middle layer is 56 × 10-5 cm/s and bottom layer is 35 × 10-5 cm/s
Only 4 out of 39 mixes have permeability higher than 125 × 10-5 cm/s New Mexico pavements are good in terms of moisture damage
050
100150200250300350400450
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Perm
eabi
lity
(x10
-5 c
m/s
)
Pavement sections
Top LayerMiddle LayerBottom Layer
COMPARISON OF PERMEABILITY AT DIFFERENT TEST MODES
In most cases, field permeability > top layer permeability > full depth permeability
No correlation between them can be made
0
50
100
150
200
250
1 2 3 4 5 6 7 15 9 10 11 12 13 14 14 16
Perm
eabi
lity
(x10
-5 c
m/s
)
Road sections
Field Permeability(cm/s) Lab k top layer (cm/s) Lab k full depth k (cm/s)
GYRATORY COMPACTED SAMPLES
SP III and SP IV mixes of New Mexico were used to prepare gyratory compacted samples
Air voids ranges from 4 to 8% were used Samples with air voids less than 6% has zero permeability The gyratory compacted samples do not represent the field in terms of
connectivity of air voids
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12 14
Perm
eabi
lity
(×10
-5 c
m/s
)
Air voids (%)
CONCLUSIONS
The following conclusions can be made from this section:
The average field permeability of selected New Mexico pavements is 178 × 10-5 cm/s which is little bit more than specification limit set by other pavements (125 × 10-5 cm/s)
A number of 12 out of 16 pavements have permeability less than 125 ×
10-5 cm/s Laboratory permeability of full depth samples is very low because of
different interface and discontinuity of flow line Only 4 out of 39 mixes have permeability less than 125 × 10-5 cm/s
Most of the New Mexico pavements have permeability in tolerable limit
PERMEABILITY AND PORES
Depending on high permeability, the following 9 pavements were selected Pavement Sections Number of layer used (Top to
bottom) US285 MP140.53 1
US70 MP289.26 2
US70 MP 282.2 2
US70 MP272.67 2
US491 MP60.5 1
NM14 MP46.9 1
NM344 MP1.8 1
NM344 MP1.82 1
NM344 MP1.84 1
TYPE OF PORES
Permeable pores
Isolated pores
Dead end pores
Effective pores Permeable pores: Pores that
continue from top to bottom of
the sample (np)
Dead end pores: Pores on top or
bottom surface that doesn’t
continue to opposite sides
Isolated pores: Pores that has no connection to top or bottom face
of the sample or not connected to permeable or dead end pores
Effective pores: Pores that are accessible by water ( sum of
permeable and dead end pores)
Different types of pores in a AC sample
Bulk specific gravity is determined as per ASTM D6752:
𝐺𝑚𝑚 =𝐴
𝐴 + 𝐷 − 𝐵 − 𝐷/𝐹
Apparent specific gravity is determined by: 𝐺𝑚𝑚 = 𝐴𝐴+𝐷−𝐶−𝐷/𝐹
Maximum specific gravity (ASTM D6857): 𝐺𝑚𝑚 = 𝐴𝐴+𝐷−𝐶−𝐷/𝐹
where A = sample dry weight in air, gm; B = weight of sealed
sample in water, gm; C = weight of sealed sample and polybag cut
under water D = polybag weight in air, gm; F = specific gravity of
polybag at 25 °C
SPECIFIC GRAVITY
PORES
Total pores: 𝑙 = 𝐺𝑚𝑚−𝐺𝑚𝑚𝐺𝑚𝑚
Effective pores: 𝑙𝑒 = 𝐺𝑚𝑚−𝐺𝑚𝑚𝐺𝑚𝑚
Isolated pores = Total pores – Effective pores
Dead end pores = Effective pore – Permeable pores
Permeable pores will be determined in later section
Permeable pores
Isolated pores
Dead end pores
Effective pores
PERMEABLE PORES
Salt meter
Permeable pores are determined using
tracer method
A salt meter is attached to a
permeameter
Salt-meter measures outflow salt
concentration with time
Tracer Test Setup
TRACER TEST
Influent salt concentration is measured using the salt-
meter
Saturated sample is placed inside the permeameter
Salt water is poured into the stand pipe and constant
water level is maintained
Ratio of effluent to influent salt concentration (C/C0) is
plotted against time
Break through time (tb) is determined which is
corresponding to C/C0 = 0.5
TRACER TEST
Laboratory permeability is calculated as: 𝑘𝑙 = 𝑞𝑞ℎ
= 𝑄𝑞𝐴ℎ
Laboratory permeable pores are calculated as: 𝑙𝑝𝑙 = 𝑡𝑚 𝑄𝐴𝑞
where q = Darcy’s velocity, cm/s; Q = discharge rate, cm3/s; A =
ross sectional area of the sample, cm2; kl = laboratory permeability, L
= length of the sample, cm; h = head, cm; tb = breakthrough time;
Numerical Example
0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80Sa
lt co
ncen
tratio
n ra
tio, C
/C0
Time (s)
NM344 MP1.8
The measured Q is 0.645 cm3/s. Sample diameter, length and constant
head are 14.2 cm, 8.1 cm, and 53.5 cm, respectively
From plot, breakthrough time
=58s
𝑘𝑙 = 𝑄𝑞𝐴ℎ
= 0.645×8.1158×53.5
=61.8×10-5
cm/s
𝑙𝑝𝑙 = 𝑡𝑚 𝑄𝐴𝑞
= 58×0.645158×8.1
=
2.915%
RESULTS AND DISCUSSIONS
R² = 0.8299
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
n pl2
(%)
npl1 (%)
R² = 0.3804 R² = 0.6118
0
2
4
6
8
10
12
14
0 5 10 15
n pl (
%)
n or ne (%)
npl vs. nnpl vs. ne
Correlation Between Different Types of Pores
Correlation of permeable pores with total pores and effective pores Correlation between npl1 and npl2
Permeable pores has a better correlation with effective
pores than total pores
6 in diameter sample has more permeable pores than 4 in
diameter sample and they have a very good correlation
Relation of Permeability with n, ne, and npl
R² = 0.2298 R² = 0.5332
R² = 0.6677
0
500
1000
1500
2000
2500
3000
0 2 4 6 8 10 12 14
kl (
x10-5
cm
/s)
n, ne or npl (%)
n vs. kl ne vs. kl npl vs. kl
Permeability increases exponentially with total, effective or
permeable pores
Permeability shows a better correlation with permeable pores
than effective pores or total pores
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Diff
eren
t typ
e of
por
es (%
) isolated porespermeable poresdead-end pores
Different Types of Pores Inside a Pavement
All samples contain more permeable pores than isolated or dead end pore For samples with high pore contents, isolated or dead end pores become
negligible
CONCLUSIONS
The finding of this section can be summarized as below:
The permeable pores of 6 in. diameter samples are higher than 4 in. diameter samples. That is, permeable pore increases with the increase of sample radius
Permeable pores show better correlation with effective pores than
total pores Permeability has a better correlation with permeable pores than
effective or total pores Permeable pore is the maximum among all pores for highly
permeable samples. The scenario may be different for low permeable samples, which has not been done in this study
FIELD PERMEABILITY MODEL
Procedure Laboratory permeability and laboratory permeable pores of 6
in. diameter samples are determined using tracer method
(described previous section)
A 4 in. diameter sample is cored out from 6 in. diameter
sample
Laboratory permeability and laboratory permeable pores of 4
in. diameter samples are also determined using tracer method
Field permeable pores and permeability is determined using
model described later
FIELD PERMEABILITY MODEL
In the laboratory, flow occurs due
to laboratory permeable pores
(“a” in the figure)
In the field, part of “c” will also
contribute to flow
Field permeable pores will be:
𝑙𝑝𝑝 = 𝑎 + 𝑓(𝑐)
In later section, a + c and c will
be represented as α and β
dy
y
p
L-y
4πrβ
1
DISTRIBUTION OF Β ALONG THE HEIGHT OF THE SAMPLE
Intensity of β is maximum near the
top and minimum near the bottom
(Hall and Ng (2001))
A triangular distribution of β is
assumed with 4πrβ at the top.
β near the bottom has more chance
to travel to bottom
A triangular probability
distribution with 1 at the bottom is
assumed
FIELD PERMEABLE PORES
Field permeable pores are determined as: 𝑙𝑝𝑝 = 𝛼 − 4𝛾3𝑟
𝛼 = 𝑟1𝑛𝑝𝑝1−𝑟2𝑛𝑝𝑝2𝑟1−𝑟2
𝛾 = (𝑛𝑝𝑝1−𝑛𝑝𝑝2)𝑟1𝑟22(𝑟1−𝑟2)
where r1 and r2 are two different sample radius & npl1 and npl2
are the corresponding laboratory permeable pores
FIELD PERMEABILITY MODEL
Model 1
𝑚 = 1𝑛𝑝𝑝1−𝑛𝑝𝑝2
𝑙𝑙 𝑘𝑝1𝑘𝑝2
𝑘𝑝 = 𝑒𝑚(𝑛𝑝𝑝−𝑛𝑝𝑝)𝑘𝑙
Model 2
µ =𝑙𝑛(𝑘𝑝1𝑘𝑝2
)
𝑙𝑛(𝑛𝑝𝑝1𝑛𝑝𝑝2
)
𝑘𝑝 = 𝑛𝑝𝑝𝑛𝑝𝑝
µ𝑘𝑙
where kl1 and kl2 are permeability corresponding to 6in. And 4 in.
diameter sample respectively
NUMERICAL EXAMPLE
For a sample from NM344 MP1.80 For a sample of radius r1 = 7.05 cm,
𝑘𝑙1 = 61.8 × 10−5 𝑐𝑚𝑠
𝑎𝑙𝑎 𝑙𝑝𝑙1 = 2.915%
For a sample of radius r2 = 5 cm cored from previous sample,
𝑘𝑙2 = 47.4 × 10−5 𝑐𝑚𝑠
𝑎𝑙𝑎 𝑙𝑝𝑙2 = 2.086%
What is the field permeability?
SOLUTION
𝛼 = 𝑟1𝑛𝑝𝑝1−𝑟2𝑛𝑝𝑝2𝑟1−𝑟2
= (7.05×2.915−5×2.086)7.05−5
= 4.94%
𝛾 = (𝑛𝑝𝑝1−𝑛𝑝𝑝2)𝑟1𝑟22(𝑟1−𝑟2)
= (2.915−2.086)×7.05×52(7.05−5)
= 7.125
Therefore,
𝑙𝑝𝑝 = 𝛼 − 4𝛾3𝑟
= 4.94 − 4×7.1253×7.05
= 3.59%
Model 1
𝑚 = 1𝑛𝑝𝑝1−𝑛𝑝𝑝2
𝑙𝑙 𝑘𝑝1𝑘𝑝2
= 12.915−2.086
ln 61.847.4
= 0.32
𝑘𝑝 = 𝑒𝑚(𝑛𝑝𝑝−𝑛𝑝𝑝)𝑘𝑙 = 𝑒0.32(3.59−2.915) × 61.8 × 10−5 = 76.7 ×
10−5 cm/s
Model 2
µ =𝑙𝑛(𝑘𝑝1𝑘𝑝2
)
𝑙𝑛(𝑛𝑝𝑝1𝑛𝑝𝑝2
)=
𝑙𝑛(61.847.4)
𝑙𝑛(2.9152.086)
= 0.793
𝑘𝑝 = 𝑛𝑝𝑝𝑛𝑝𝑝
µ𝑘𝑙 = 3.59
2.915
0.79361.8 × 10−5 = 72.9 × 10−5 cm/s
RESULTS AND DISCUSSIONS
y = 1.3294x + 21.831 R² = 0.9723
0
500
1000
1500
2000
2500
0 500 1000 1500 2000 2500
k f (x
10-5
cm
/s)
kl (x10-5 cm/s)
kf vs. kl
Correlation between predicted and laboratory permeability
Predicted permeability shows very good correlation with laboratory
permeability
Use of this correlation will eliminate the work associated with coring and
tracer test
Laboratory, Predicted and Measured Field Permeability
For pavements with OGFC, measured k is very high compared to predicted k. A
shift factor is not possible to determine in this case
For pavements without OGFC, measured k is less than predicted k
Anova analysis gives an intercept of 9.43×10-5 cm/s with slope 0.132.
The mean of error and standard deviation of error are 12.3% and 6.4% which are
pretty good.
0100200300400500600700800900
1000
Perm
eabi
lity
(×10
-5 c
m/s
)
Pavement sections
Laboratory k
Predicted k
Measured k
Pavements with Pavements without
020406080
100120140160
0 50 100 150
Pred
icte
d pe
rmea
bilit
y (x
10-5
cm
/s)
Field measured permeability (x 10-5 cm/s)
Pavements without OGFC
The study of this chapter can be summarized as below: Field and laboratory permeability doesn’t show any correlation The model proposed initially gives field permeability of an
ideal pavement which has no OGFC, fully saturated, single layer with no coating etc. In reality, no pavement is ideal therefore, the model cannot be used directly
A shift factor is used with the prediction which gives results
very close to the measured permeability, but it works only for pavements without OGFC. Pavements with OGFC, the model does not work
CONCLUSIONS
PERMEABILITY AND MOISTURE DAMAGE
Moisture damage is measured by the ratio of wet to dry
sample’s Indirect Tensile Strength (IDT)
Wet conditioning is done by the AASHTO T 283 and
Moisture Induced Sensitivity Testing (MIST)
Moisture Damage Testing
AASHTO T 283 Wet Conditioning
MIST Wet Conditioning
Dry Conditioning
AASHTO T 283
Sample is saturated using a vibro-deairator or Corelok
Saturated sample is placed inside a Ziplock bag and
inside a freezer
Damage occurs due to pressure by increased volume
of water due to icing
Sample is thawed on hot water bath, causing more
damage
MIST
Chamber is pressurized to 40psi and release for
3500 cycles
Increase of chamber pressure cause increase in pore
pressure of the sample and damage occurs
Sample is placed inside
the chamber of MIST
Chamber is filled with
water and closed MIST
IDT AND TENSILE STRENGTH RATIO (TSR)
IDT testing
Sample is compressed along the
diagonal
Tensile failure perpendicular to loading
direction occurs
IDT is determined using the following
equation: 𝐼𝐷𝐼 = 2𝑃𝜋𝐷𝑞
TSR is determined using equation:
𝐼𝑇𝑇 = 𝐼𝐷𝐼𝑤𝑤𝑤𝐼𝐷𝐼𝑑𝑑𝑑
Minimum allowable TSR is 0.8
HAMBURG WHEEL TRACKER TEST
Hamburg wheel tracker was first used in Germany in mid 70s Cylindrical or slab samples are placed in the mold Metal wheels of 158lbs weight roll over the sample for 20000
cycles After several cycles, the rut depth increases suddenly which is a
measure of moisture damage susceptibility
SPECIFICATION BY OTHER STATES
DOT Stripping inflection limit Colorado 10000 California 5000 (Conventional binder)
10000 (Polymer modified binder)
DOT/ Agency Max. rut depth Number of cycles Hamburg, Germany 4 20000 Colorado 4 10000
10 20000 Texas 12.5 10000 (PG-64 or less)
12.5 15000(PG-70) 12.5 20000 (PG-76 or more)
California 8 20000 (Conventional binder) 11 20000 (Polymer modified binder)
RESULTS AND DISCUSSIONS
Field Damage with Field Permeability
0
50
100
150
200
250
300
9 10 11 12 13 14 15 16
Perm
eabi
lity
(x10
-5 c
m/s
)
(b) Bad performing pavements
Ave. k = 298×10-5
cm/s
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8
Perm
eabi
lity
(x10
-5 c
m/s
)
(a) Good performing pavement sections
Ave k = 62.7E-5
Bad performing pavement sections have almost 5
times higher field permeability than good performing
sections
Field Damage with Laboratory Full Depth Permeability
0102030405060708090
100
1 2 3 4 5 6 7 8
Perm
eabi
lity
(×10
-5 c
m/s
)
(a) Good performing sections
Ave. k =4.15×10-5cm/s 0
102030405060708090
100
9 10 11 12 13 14 15
Perm
eabi
lity
(×10
-5 c
m/s
)
(b) Bad performing sections
Ave. k = 69.7× 10-5 cm/s
All the full depth permeabilities are almost zero, because of different
coating and discontinuity of flow line at different layers
Pavements 13 and 14 are single layered pavements
Bad performing pavements have higher full depth permeability than
good performing pavements
Field Damage with Laboratory Permeability of Individual Layer
0
50
100
150
200
250
300
Top Layer Middle Layer Bottom Layer
Perm
eabi
lity
(x10
-5 c
m/s
)
(b) Different layers of bad performing sections
9 10 11 12
13 14 15 16
Ave. k = 78.6 × 10-5 cm/s
0
50
100
150
200
250
300
Top Layer Middle Layer Bottom Layer
Perm
eabi
lity
(×10
-5 c
m/s
)
(a) Different layers of good performing pavement section
1 2 3 4 5 6 7 8
Ave. k =22.9×10-5 cm/s
Permeability of bad
performing sections is
more than 3 times higher
than good performing
pavements
Permeability of top layer
is higher than middle or
bottom layer
MIST Damage with Permeability
R² = 0.0886
0
0.2
0.4
0.6
0.8
1
0 100 200 300
MIS
T TS
R
(c) Permeability of layered samples (×10-5 cm/s)
R² = 0.0166
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500
MIS
T TS
R
(a) Field permeability (×10-5 cm/s)
R² = 0.0811
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30
MIS
T TS
R
(b) Laboratory full depth k (×10-5 cm/s)
MIST shows a bad correlation with
field or laboratory permeability
Samples are collected from all over
New Mexico. Aggregate type, binder
grade and chemistry was not
considered.
AASHTO T 283 Damage with Permeability
R² = 0.3808
00.10.20.30.40.50.60.70.80.9
1
0 50 100 150 200 250 300 350
TSR
Permeabillity (× E-5 cm/s)
R² = 0.0349
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200 250 300
AA
SHTO
T 2
83 T
SR
(a) Field permeability (×10-5 cm/s)
R² = 0.1721
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25
AA
SHTO
T 2
83 T
SR
(b) Full depth permeability (×10-5 cm/s)
Field or full depth permeability
doesn’t show any correlation with the
AASHTO T 283 damage
AASHTO T 283 shows a better
correlation with samples separated at
layers
00.10.20.30.40.50.60.70.80.9
1
1TL
10B
L10
ML
10TL
11B
L11
ML
11TL
12TL
13B
L13
ML
13TL
14B
L14
BL
14TL
15TL
16B
L16
ML
16TL 1B
L2B
L2M
L2T
L3T
L4M
L4T
L5B
L5M
L5T
L6B
L6M
L6T
L7B
L7M
L7T
L8B
L8T
L9B
L9M
L9T
L
TSR
Pavement Sections ( TL = Top layer, ML = Middle Layer, BL = Bottom Layer)
T 283 TSR MIST TSR
AASHTO T 283 and MIST TSR
The AASHTO T 283 values are less than MIST TSR values indicating
more damage occurs in the AASHTO T 283 conditioning
Few samples have higher AASHTO T 283 TSR than MIST TSR
Field Damage with AASHTO T 283 Damage
0
0.2
0.4
0.6
0.8
1
Top layer Middle Layer Bottom layer
TSR
Good pavements
12345678
0
0.2
0.4
0.6
0.8
1
Top layer Middle layer Bottom layer
TSR
Bad pavements
910111213141516
Most of the good pavements have higher TSR value
Most of the bad pavement have TSR value less than 0.8
However, 11 mixes from good pavements have TSR less than 0.8
3 mixes from bad pavements have more TSR than 0.8
Therefore, AASHTO T 283 doesn’t represent the field exactly.
Field Damage with MIST Damage
0
0.2
0.4
0.6
0.8
1
Top Layer Middle layer Bottom layer
TSR
Good pavements
12345678
0
0.2
0.4
0.6
0.8
1
Top layer Middle layer Bottom layer
TSR
Bad pavements
910111213141516
Most of the good pavements have TSR higher than 0.8
Many mixes from bad pavements have TSR higher than 0.8
MIST doesn’t represent the field exactly
Permeability and Moisture Damage of Laboratory Compacted Samples
R² = 0.0101
R² = 0.1089
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0 10 20 30 40 50
TSR
Permeability (x 10-5 cm/s )
Series1
MIST
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1 2 3 4 5 6 7
TSR
Samples
AASHTO T 283
MIST
SP III SP IV
No correlation between permeability and moisture damage is obtained MIST and AASHTO T 283 causes similar damage
Correlation of TSR with Dead End and Permeable Pores
R² = 0.0393
R² = 0.1604
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 1 2 3 4
TSR
Dead-end pores (%)
MIST TSR vs. dead-end voids
AASHTO T 283 TSR vs. dead-end air voids
R² = 0.5019
R² = 0.6122
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
4 6 8 10 12
TSR
Permeable pores (%)
MIST TSR vs. Pearmeable pores
AASHTO T 283 TSR vs. permeable pores
Moisture damage increase with the increase of dead end pore
Moisture damage decrease with the increase of permeable pores
The correlation is not that good as other factors like asphalt grade,
aggregate type were not maintained constant
Hamburg Wheel Tracker Results On each sample, rut depth is measured at 6 equidistance locations The plot for rut depth vs. number of wheel passes for 8 good and 8 bad
performing pavements is shown in next figures Each line in the plot is the average of these six reading
-20-18-16-14-12-10-8-6-4-20
0 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 2 >20000 2 1.6 >20000 3 3 >20000 4 4 >20000
Average 2.6 >20000 Ave. initial compaction = 1 mm
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 10 >20000 2 15.5 >20000 3 15.5 17500 4 19.5 18000
Average 15.5 17500 Ave. initial compaction = 1.5 mm
Pavement section 1
Pavement section 3
-20-18-16-14-12-10-8-6-4-20
0 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 2.5 >20000 2 4 >20000 3 2.5 >20000 4 3.7 >20000
Average 3.1 >20000 Average initial compaction = 1 mm
Pavement section 4
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 6.3 >20000 2 5.3 >20000 3 2.8 >20000 4 2.8 >20000
Average 4.2 >20000 Average initial compaction = 2 mm
Pavement section 5
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 5.9 >20000 2 11.4 >20000 3 9 >20000 4 7.2 >20000
Average 8.1 >20000 Average initial compaction = 6 mm
Pavement section 6
-20-18-16-14-12-10-8-6-4-20
0 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
-20-18-16-14-12-10-8-6-4-20
0 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 4.9 >20000 2 10 >20000 3 9.8 >20000 4 >20 >20000
Average 8 >20000 Average initial compaction = 1 mm
Comment: Sample 4 was neglected to find Ave.
Pavement section 7
Sample no.
Rut depth for 20000 passes (mm)
Inflection point (Passes)
1 7.8 >20000 2 11 >20000 3 10.5 >20000 4 8 >20000
Average 8.8 >20000 Average initial compaction = 3.8 mm
Comment: Sample 3 was neglected to find Ave.
Pavement section 8
Bad Pavements
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000
passes (mm) Inflection point
(Passes) 1 5 >20000 2 5 >20000 3 5 >20000 4 17.5 >20000
Average 5 >20000 Average initial compaction = 1 mm
Comment: Sample 4 was neglected to find Ave. Pavement section 9
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000 passes
(mm) Inflection point
(Passes) 1 7 >20000 2 17.5 >20000 3 7.5 >20000 4 8 >20000
Average 7.5 >20000 Average initial compaction = 1 mm
Comment: Sample 2 was neglected to find Ave. Pavement section 10
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000
passes (mm) Inflection point
(Passes) 1 2.3 >20000 2 3.3 >20000 3 4.6 >20000 4 7 >20000
Average 3.4 >20000 Average initial compaction = 1 mm
Comment: Sample 4 was neglected to find Ave. Pavement section 11
-10
-8
-6
-4
-2
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of cycles
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000 passes
(mm) Inflection point
(Passes)
1 6 >10000 2 7 >10000 3 7 >10000 4 8.6 >10000
Average 7 >10000 Average initial compaction = 1 mm
Pavement section 12
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000 passes
(mm) Inflection point
(Passes) 1 5 >20000 2 7.6 >20000 3 4.5 >20000 4 7.6 >20000
Average 6.1 >20000 Average initial compaction = 1 mm
Pavement section 13
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000
passes (mm) Inflection point
(Passes) 1 12 >20000 2 11 >20000 3 5 >20000 4 9.8 >20000
Average 8.2 >20000 Average initial compaction = 1 mm
Comment: Sample 1 was neglected to find Ave.
Pavement section 14
-20
-15
-10
-5
00 5000 10000 15000 20000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000
passes (mm) Inflection point
(Passes) 1 7 >20000 2 10.8 >20000 3 11 >20000 4 12 >20000
Average 10.2 >20000 Average initial compaction = 1 mm
Pavement section 15
-30
-25
-20
-15
-10
-5
00 2000 4000 6000 8000
Rut
dep
th (m
m)
Number of passes
Sample 1 Sample 2Sample 3 Sample 4Average
Sample no. Rut depth for 20000
passes (mm) Inflection point
(Passes)
1 >24 ≈5000 2 >12 >10000 3 >28 ≈5000 4 >24 ≈5000
Average >20 ≈5000 Average initial compaction < 1 mm
Pavement section 16 Summary table
Pavement Section
Initial Compaction (mm)
Rut depth (mm) Inflection point Comment
1 1 2.6 >20000 ok
3 1.5 15.5 17500 Rut depth- not ok, Stripping potential –ok
4 1 3.1 >20000 Ok
5 2 4.2 >20000 Ok
6 6 8.1 >20000 Ok
7 3.8 8.8 >20000 Ok
8 1 8 >20000 Ok
Summary for good pavements
Summary for bad pavements Pavement Section Initial Compaction
(mm) Rut depth (mm) Inflection point Comment
9 1 5 >20000 Ok
10 1 7.5 >20000 Ok
11 1 3.4 >20000 Ok
12 1 7 >10000 Ok
13 1 6.1 >20000 Ok
14 1 8.2 >20000 Ok
15 1 10.2 >20000 Ok
16 1 >20 ≈5000 Fail
Summary of HWT Tests
It is seen that all the good pavements satisfies the stripping potential criteria set by other DOTs
Good pavements are not susceptible to moisture damage Pavement section 3 fails to satisfy the rutting criteri Only one of the bad pavements has rutting and stripping
Potential Hamburg wheel tracking doesn’t yield significant result for
New Mexico Pavements
The following conclusion can be made from this section: Bad performing pavements have higher permeability than good
performing pavements. Permeability increases the potential of moisture damage
MIST TSR doesn’t show any kind of correlation with permeability.
AASHTO T 283 TSR shows a better correlation with permeability MIST does less damage to a sample than AASHTOO T 283 does Moisture damage increase with the increase of dead-end pores and
decrease with the increase of permeable pores
Hamburg wheel tracking does not show the stripping potential properly
CONCLUSIONS
PERMEABILITY SPECIFICATION Procedure: Critical permeability limit is determined based on critical air voids which
is determined as follows: Permeability vs. air voids are plotted on a graph Tangents are drawn on at initial and final portion of the regression line of the plot Perpendicular is drawn from the intersection of tangents to the regression line The point of intersection between regression line and the perpendicular gives the “critical air void”. Before the “critical air void”, increase of air void yields more isolated voids than interconnected voids resulting less increase in permeability After the “critical air void”, increase of air void yields more interconnected voids than isolated voids resulting high increase in permeability Permeability at this air void is the maximum allowable permeability or critical permeability.
Critical Permeability for Laboratory Testing of Field Cores (Individual Data)
% Air Void Permeability(cm/s)
Critical 10 90x10-5
Lower limit 7 1x10-5
Upper Limit 4 1.15x10-7
Critical Permeability for Laboratory Testing of Field Cores (Average Data)
% Air Void Permeability(cm/s) Critical 9.2 125x10-5
Upper limit 7 6.6x10-5 Lower Limit 4 1.8x10-7
Critical Permeability from Full Depth Sample Testing
Condition % Air voids Permeability (cm/s) Critical 7.6 6x10-5
Upper Limit 7 2x10-5 Lower Limit 4 8x10-8
DECISION ON CRITICAL PERMEABILITY VALUE
State Critical Permeability(cm/s)
New Mexico(Proposed) 125E-5
Florida 125E-5
Oklahoma 125E-5
Virginia 125E-5
Critical permeability of 125E-5cm/s is proposed for NM as it seems more reasonable comparing to other states.
Conclusions
The following conclusions can be made from this study: Both field and laboratory permeability of New Mexico
pavements are very close to specification limits. Field permeability is higher than laboratory permeability for pavement with OGFC. For pavement without OGFC, the scenario is opposite. Permeability of full length sample is almost zero. For laboratory compacted samples, permeability is zero for sample with less than 6% air void
Combination of a permeameter with a salt meter can be used to determine permeable pores of AC samples. Asphalt concrete sample’s permeability varies in radial direction due to increase in permeable pore in radial direction. Permeability shows a good relationship with permeable pores, than total pores
CONCLUSIONS AND RECOMMENDATIONS
Conclusions Field permeability is always higher than the laboratory
permeability except for single layered pavements. Field permeability cannot be correlated with laboratory permeability on the basis of experiments only. Therefore, an analytical model is developed to determine field permeability by testing cores in the laboratory. The analytical model predicts field permeability of asphalt pavements reasonably well if the pavement is not covered with OGFC
All types of permeability of bad performing pavements are much
higher than the permeabilities of good performing pavements. Hence, permeability increases the potential of moisture damage. MIST TSR shows a bad correlation with permeability. The AASHTO T 283 TSR shows a better correlation with permeability. Moisture damage increase with the increase of permeability.
Conclusions
The maximum allowable permeability for New Mexico pavements is obtained as 125 × 10-5 cm/s, which is exactly equal to the permeability specification proposed by other states
RECOMMENDATIONS
Different type of pores and their correlation to moisture
damage should be evaluated to a specific mix to get a better
correlation
A small single layer test pavement can be constructed over a
highly permeable sand layer with the facility of saturation.
Field permeability of this test pavement should exactly match
with the predicted permeability
ACKNOWLEDGEMENTS
NMDOT Research Bureau
Project advocate Mr. James Gallegos and Parveez Anwar,
Project Manager Keli Daniell, Jeff Mann, Robert McCoy
and the Project Technical Panel
John Gillentine and his field exploration crew
Naomi Waterman and Shahidul Amin Faisal and Valarie
McCoy
Thank You