PERMEABILITY AND MOISTURE DAMAGE CHARACTERISTICS …dot.state.nm.us/content/dam/nmdot/Research/NM11MSC-04_Multimedi… · PERMEABILITY AND MOISTURE DAMAGE CHARACTERISTICS OF ASPHALT

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


US70MP282.2TL US70MP282.2ML

US70MP282.2BL US70MP272.67TL


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


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


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


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)


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 no.



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 no.



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 no.



1 6.3 >20000 2 5.3 >20000 3 2.8 >20000 4 2.8 >20000


Pavement section 5

-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes


Sample no.



1 5.9 >20000 2 11.4 >20000 3 9 >20000 4 7.2 >20000


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


-20-18-16-14-12-10-8-6-4-20

0 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes


Sample no.



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.



1 7.8 >20000 2 11 >20000 3 10.5 >20000 4 8 >20000

Average 8.8 >20000 Average initial compaction = 3.8 mm


Pavement section 8

Bad Pavements

-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes


Sample no. Rut depth for 20000

passes (mm) Inflection point

(Passes) 1 5 >20000 2 5 >20000 3 5 >20000 4 17.5 >20000


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 no. Rut depth for 20000 passes

(mm) Inflection point

(Passes) 1 7 >20000 2 17.5 >20000 3 7.5 >20000 4 8 >20000



-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes




(Passes) 1 2.3 >20000 2 3.3 >20000 3 4.6 >20000 4 7 >20000



-10

-8

-6

-4

-2

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of cycles




(Passes)

1 6 >10000 2 7 >10000 3 7 >10000 4 8.6 >10000


Pavement section 12

-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes




(Passes) 1 5 >20000 2 7.6 >20000 3 4.5 >20000 4 7.6 >20000


Pavement section 13

-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes




(Passes) 1 12 >20000 2 11 >20000 3 5 >20000 4 9.8 >20000



Pavement section 14

-20

-15

-10

-5

00 5000 10000 15000 20000

Rut

dep

th (m

m)

Number of passes




(Passes) 1 7 >20000 2 10.8 >20000 3 11 >20000 4 12 >20000


Pavement section 15

-30

-25

-20

-15

-10

-5

00 2000 4000 6000 8000

Rut

dep

th (m

m)

Number of passes




(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

Documents

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