1sk1bc-Comparative Evaluation of the Stiffness Properties of Warm-Mix Asphalt Technologies and E Predictive Models

8/13/2019 1sk1bc-Comparative Evaluation of the Stiffness Properties of Warm-Mix Asphalt Technologies and E Predictive Mo

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Habtamu Zelelew, Matthew Corrigan, Satish Belagutti, and Jeevan RamakrishnaReddy

1

Duplication of this paper for publication or sale is strictly prohibited without prior1written permission of the Transportation Research Board2

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4

Comparative Evaluation of the Stiffness Properties of5

Warm-Mix Asphalt Technologies and |E*| Predictive Models67

8

Habtamu Zelelew, PhD (Corresponding Author)9ESC Inc, FHWA10

Office of Pavement Technology111200 New Jersey Ave., SE12Washington, DC 20590,13Phone: (202) 366-660614

e-mail: [email protected]

Matthew Corrigan, P.E.17Federal Highway Administration18Office of Pavement Technology19

1200 New Jersey Ave., SE20Washington, DC 2059021Phone: (202) 366-154922


Satish Belagutti25ESC Inc, FHWA TFHRC26

6300 Georgetown Pike, McLean, VA 2210127Phone: (202) 493-310328


Jeevan RamakrishnaReddy31ESC Inc, FHWA TFHRC32

6300 Georgetown Pike, McLean, VA33Phone: (202) 256-592834

e-mail:[email protected]

No. of Words = 3235 + 8*500 = 7235 < 750038

39

Transportation Research Board Committee40AFK30: Characteristics of Nonasphalt Components of Asphalt Paving Mixtures41

42For Presentation at the 91

stAnnual Meeting43

44

October 31 20114546

47

TRB 2012 Annual Meeting Paper revised from original submittal


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ABSTRACT1

Warm-mix Asphalt (WMA) has gained popularity due to rising energy costs and potential2

reductions in carbon dioxide and carbon dioxide equivalent emissions. In this paper, a3

comprehensive laboratory evaluation of WMA technologies stiffness properties and comparison4

of three |E*| predicting models (Witczak 1-37A, Witczak 1-40D, and Hirsch) are presented. A5

total of nine WMA technologies were included; six foaming processes (Accu-Shear, Advera

,6

Aspha-min, Aquablack

, Low Emission Asphalt (LEA), and Gencor), two chemical additives7

(Evothermand Rediset

), and an organic additive (Sasobit

). The rheological properties of the8

asphalt binders were characterized using the dynamic shear rheometer device at four test9

temperatures (4.4, 21.1, 37.8, and 54.4C) and multiple frequencies (0.016 to 25 Hz). The asphalt10

mixture performance tester was used to capture the stiffness properties of the asphalt mixtures11

using four temperatures (4.4, 21.1, 37.8, and 54.4C) and six frequencies (25, 10, 5, 1, 0.5, and12

0.1 Hz). The stiffness properties of the WMA technologies as well as their control13

binders/mixtures were evaluated through the use of master curves (both shear modulus and14

dynamic modulus). Compared to the control binder and mixture specimens, lower stiffness15

values were observed for the WMA technologies. Overall, reasonable |E*| predictions of the16

plant produced WMA technologies were obtained when the Hirsch model was utilized followed17

by the Witczak 1-40D model and the Witczak 1-37A model.18

19

KEYWORDS: Warm-mix asphalt, shear modulus, dynamic modulus, and |E*| predictions20

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INTRODUCTION1

In recent years, Warm-mix Asphalt (WMA) has gained popularity due to rising energy2

costs, potential reductions in carbon dioxide and carbon dioxide equivalent emissions, and the3

need for sustainable materials. WMA is the name given to a variety of technologies that allow4

producing asphalt mixtures to lower temperatures at which the material is mixed, compacted, and5

placed on the roadways. Some WMA technologies have potential benefits in reducing the binder6

viscosity as well as reducing the short term aging of the mixture during production ( 1, 2).7

Another benefit of WMA is that the improved workability which allows incorporation of higher8

percentages of Reclaimed Asphalt Pavement (RAP) or Reclaimed Asphalt Shingles (RAS) in the9

asphalt mixture (2). There is a widespread concern in pavement community however that the10

reductions in binder viscosity and production temperatures may lead WMA mixtures to exhibit11

lower stiffness properties and consequently prone to rutting as compared to the conventional hot-12

mix asphalt (HMA) mixtures.13

The first trial WMA field projects were constructed in 2004 in Florida and North14

Carolina. To date, over forty-five states and ten Canadian provinces have constructed WMA15

demonstration projects in their jurisdictions (2). Since then, several of WMA technologies have16

emerged in the US market. There is a need to fully understand the properties of WMA17

technologies including their interaction with the asphalt binder and consequently their potential18

affect on pavement performance. In 2005, the Federal Highway Administration (FHWA) in19collaboration with the National Asphalt Pavement Association (NAPA) formed the WMA20

technical working group in order to address these challenges and implement WMA technologies21

successfully.22

The FHWA Office of Pavement Technology introduced the Asphalt Mixture23

Performance Tester (AMPT) equipment for conducting performance-based evaluation of asphalt24

concrete mixtures. The stiffness and deformation properties of asphalt mixes can be evaluated25

using this device respectively through the dynamic modulus and flow number tests. The dynamic26

modulus of an asphalt mixture, identified by |E*|, is a response developed under sinusoidal27

loading conditions tested at multiple frequencies and multiple temperatures. Among others,28

accuracy and repeatability of |E*| measurements can be significantly influenced by the material29

properties and test conditions (e.g., temperature, confinement, rate of loading, tuning/calibration,30

and specimen conditioning). When specimens are tested under higher test temperatures and/or31



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lower loading frequencies, the strain measuring gauge point locations can loosen and1

consequently high variations in the measured |E*| are observed.2

|E*| is also a crucial input to the AASHTOWare DARWin-ME (formerly the3

Mechanistic-Empirical Pavement Design Guide (ME PDG)) which requires laboratory measured4

(Level 1) or predicted (Level 2 and 3) dynamic modulus for estimating pavement performance5

(3). Over the past several years, various HMA |E*| predictive models have been developed (4-9).6

The three most popular models include: the NCHRP 1-37A project (referred in this paper as the7

Witczak 1-37A model) (4); the NCHRP 1-40D project (referred in this paper as the Witczak 1-8

40D model) (5); and the Hirsch model (6). Several studies utilized these models to predict HMA9

|E*| over a range of temperatures, rate of loading, and aging conditions (10-13).10

This paper presents a comprehensive laboratory evaluation of WMA technologies11

stiffness properties. It underscores identifying the effects of WMA technologies on12

binder/mixture stiffness properties. A comparative assessment of the WMA |E*| predicting13

models (Witczak 1-37A, Witczak 1-40D, and Hirsch) is also presented. The study included nine14

WMA demonstration projects in eight states visited by the FHWA Mobile Asphalt Trailer15

Laboratory (MATL) program over the past five years.16

17

OBJECTIVES18

The primary objectives of this study were to:19 Identify the effects of WMA technologies on binder stiffness properties.20 Identify the effects of WMA technologies on mixture stiffness properties.21 Compare the Witczak 1-37A model, the Witczak 1-40D model, and the Hirsch model in22

predicting plant produced WMA |E*|.23

In order to achieve these objectives, laboratory tests were conducted using the Dynamic Shear24

Rheometer (DSR) and AMPT devices respectively to capture the rheological properties of25

asphalt binders and characterize the stiffness properties of the asphalt mixtures.26

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MATERIALS1

The WMA technologies included six foaming processes (Accu-Shear, Advera

, Aspha-2

min, Aquablack

, Low Emission Asphalt (LEA), and Gencor), two chemical additives3

(Evotherm and Rediset

), and an organic additive (Sasobit

). The base binder grade ranged4

from PG 58-34 to PG 76-22. Ten mix designs meeting the respective state DOT specification5

were included, eight Superpave mixes containing 9.5 mm, 12.5 mm, 19 mm, and 25 mm and two6

19 mm Hveem mixes. The project locations covered a wide range of traffic levels as the design7

gyrations (Ndesign) ranged from 55 to 125. All binder tests were conducted at the AMRL-8

accredited Asphalt Binder Testing Laboratory (ABTL) operated by the FHWA Office of9

Pavement Technology. The mixture volumetrics and AMPT performance tests were performed10

by the Mobile Asphalt Mixture Testing Laboratory (MAMTL).11

12

BINDER TESTING13

The AASHTO T164 Standard Method of Test for Quantitative Extraction of Asphalt14

Binder from Hot Mix Asphalt (HMA) test protocol was used for extraction of asphalt binders15

from the plant produced asphalt mixture specimens. In addition, the ASTM D5404 Standard16

Practice for Recovery of Asphalt from Solution Using the Rotary Evaporatortest protocol was17

utilized to recover the asphalt binder specimens. This test method recommends using18

Trichloroethylene solvent for extraction and recovery process. However, the FHWA binder19

laboratory has been using an 85% toluene and 15% of ethanol mixture for extraction and20

recovery process. The rheological properties of the extracted and recovered asphalt binders were21

then measured following the AASHTO T315 Standard Method of Test for Determining the22

Rheological Properties of Asphalt Binder Using a Dynamic Shear Rheometer (DSR) test23

protocol. The DSR testing consisted of 25 mm parallel plate geometry and 1 mm gap setting. The24

asphalt binder sources included lab blended and plant supplied specimens. The Silverson high25

shear mixer was used to blend the base binder and the WMA technology in the laboratory. The26

specified dosage rates of the WMA technology was added gradually into the base binder.27

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Test Results1

Shear Modulus Master Curve2

The frequency sweep tests were conducted to evaluate the stiffness properties of the3

control binders and binders containing WMA technologies. The binder specimens were tested4using test temperatures of 4.4, 21.1, 37.8, and 54.4 C over a wide range of loading frequencies5

0.1 to 157.1 rad/s (i.e., 0.016 to 25 Hz). As described later, the asphalt mixture dynamic modulus6

tests were also conducted using the same set of test temperatures. Each of the frequency sweep7

test data was then shifted to a reference temperature of 21.1 C and fitted with generalized8

logistic function developed by Pellinen, Witczak, and Bonaquist (14).9

Figures 1 and 2 present the comparison of shear modulus |G*| master curves for the10

control binder and WMA technologies included in the study. Asphalt binders with higher |G*|11

mostly improve shear deformation resistance. It is shown in these figures that the asphalt binders12

containing the organic additive Sasobitmeasured high stiffness. The Accu-Shear

and Rediset

13

technologies measured slightly higher stiffness as compared to their control binders primarily at14

the low reduced frequency ranges (i.e., below 10 Hz). The other WMA technologies (Advera,15

Aspha-min, and Evotherm

) measured comparably similar stiffness values as their control16

binders when the lower reduced frequency range is considered. For the PA0986 project, the LEA17

and Gencor technologies demonstrated higher stiffness as compared to the control binder when18

the high reduced frequency ranges are considered. The differences in the stiffness properties19

amongst these WMA technologies could be explained from the differences in base binder,20

dosage rates, the WMA technology used, and the inherent variability in the DSR test procedures.21



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(a) (b)1

2

(c) (d)3

4

FIGURE 1 Shear modulus master curve; (a) MO0672, (b) CO0777, (c) WY0778, and (d) TX0985.5

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearModulus,

|G*|(Pa)

Reduced Frequency (Hz) (TRef= 21.1 C)

PG 70-22

Sasobit

Aspha-min

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearModulus,

|G*|(Pa)


PG 58-28

Advera

Sasobit

Evotherm

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearM

odulus,

|G*|(Pa)


PG 58-34

Advera

Sasobit

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearM

odulus,

|G*|(Pa)


PG 70-22

Rediset

TRB 2012 Annual Meeting Paper revised from original submit


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(a) (b)1

2

(c)3

4

FIGURE 2 Shear modulus master curve; (a) PA0986, (b) LA1088, and (c) IN1099.5

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearModulus,

|G*|(Pa)


PG 64-22

Advera

Sasobit

LEA

Gencor

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearModulus,

|G*|(Pa)


PG 64-22

Accu-Shear

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1 .E-0 6 1 .E -0 4 1 .E -0 2 1 .E+0 0 1.E+02 1 .E+0 4 1 .E+0 6 1.E+08

ShearM

odulus,

|G*|(Pa)


PG 64-22

Accu-Shear



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ASPHALT CONCRETE MIXTURE TESTING1

Specimen Preparation2

Plant produced asphalt mixtures for dynamic modulus specimens were sampled from3

haul trucks. Asphalt specimens were immediately fabricated without reheating or additional4oven conditioning to eliminate additional mixture aging. The asphalt mixtures were then5

compacted to 8.5% air voids in the gyratory compactor in order to achieve the 7.0+0.5%6

targeted air voids for the cored and trimmed test specimen. The performance test specimens7

were cored from the center 100 mm of a 150 mm diameter specimen and the sample ends8

were trimmed from a height of 180+ mm down to 150 mm. The MATL mix design9

replication (MDR) samples were oven conditioned for 4 hours at 135C.10

11

Dynamic Modulus Test12

Four test replicates per sample were used for performance testing. Since the dynamic13

modulus test is non-destructive at low temperatures, the same set of four replicates were14

tested at the three lower temperatures (4.4, 21.1, and 37.8C), while another set of four15

replicates were tested at the high temperature (54.4C). Six loading frequencies were used16

25, 10, 5, 1, 0.5, and 0.1 Hz. The dynamic modulus tests were performed from the lowest17

temperature to the highest temperature and from the highest frequency to the lowest18

frequency. The axial stress needed in the unconfined test to produce a target microstrain of19

10015 was used. The dynamic modulus |E*| was calculated by dividing the maximum peak-20

to-peak stress by the recoverable peak-to-peak strain.21

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Test Results23

Dynamic Modulus Master Curve24

The dynamic modulus test data was used to construct master curves for each of the25

test specimen at a reference temperature of 21.1C. The data was then shifted along the26

frequency axis to form a single |E*| master curve using the sigmoidal function given in ME27

PDG (3).28

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Figures 3 through 5 present comparison of |E*| master curves of the control HMA and1

WMA mixtures for all the projects included in the study. Overall, the |E*| master curve plots2

exhibited similar shape/trend for a wide range of frequencies. The stiffness properties of all3

of the asphalt mixtures presented in these figures decreased with an increase in test4

temperature and increased with an increase in loading frequency. Asphalt mixtures with5

higher |E*| mostly improve stability and rutting resistance. In general, compared to the6

control HMA mixtures, lower stiffness values were observed for the WMA technologies7

prepared with foaming processes followed by the chemical additives. The reduction in8

stiffness is more pronounced for the asphalt mixtures with Advera

and Aspha-min9

technologies and therefore these mixes may be more susceptible to rutting. This is a concern10

during the early life of the pavement if high temperatures are encountered and heavy traffic11

loading is placed on the pavement before it can age and stiffen in place on the roadway. The12

WMA mixtures containing organic additive Sasobitexhibited higher stiffness, particularly13

at lower and intermediate frequency ranges. In these figures, the MATL mix design14

replicates (MDR) mixtures measured relatively higher stiffness (except for MO0987 project)15

as compared to the plant produced HMA mixtures due to additional oven conditioning (416

hours at 135C). The differences in the stiffness properties of these WMA mixtures could be17

explained through, among others, the differences in volumetric properties, binder rheological18

properties, WMA dosage rates, aggregate shape properties (e.g., angularity and texture),19

production temperatures, and plant aging.20

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(a)1

(b)2

(c)3

FIGURE 3 Dynamic modulus master curve; (a) MO0672, (b) CO0777, and (c) WY0778.4

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

WMA (Sasobit)

WMA (Aspha-min)

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

WMA (Advera)

WMA (Sasobit)

WMA (Evotherm)

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

WMA (Advera)

WMA (Sasobit)



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(a)1

(b)2

(c)3

4

FIGURE 4 Dynamic modulus master curve; (a) MN0884, (b) TX0985, and (c) PA0986.5

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA Wear

HMA Nonwear

WMA (Evotherm) Wear

WMA (Evotherm) Nonwear

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

WMA (Rediset 2)

WMA (Rediset 10)

WMA (Rediset 12)

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

HMA (MDR)

WMA (Advera)

WMA (Sasobit)

WMA (LEA)

WMA (Gencor)



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(a)1

(b)2

(c)3

4

FIGURE 5 Dynamic modulus master curve; (a) MO0987, (b) LA1088, and (c) IN1090.5

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

HMA (MDR)

WMA (Aquablack 6)

WMA (Aquablack 7)

WMA (Aquablack 8)

WMA (Aquablack 10)

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA 12.5mm

HMA (MDR) 12.5mm

WMA (Accu-Shear) 12.5mm

HMA 19mm

HMA (MDR) 19mm

WMA (Accu-Shear) 19mm

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08

DynamicModulus,|

E*|(MPa)


HMA

WMA (Accu-Shear 1)

WMA (Accu-Shear 2)

WMA (Accu-Shear 3)



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|E*| PREDICTIONS1

This paper also included predictions of |E*| through the use of the Witczak 1-37A,2

Witczak 1-40D, and Hirsch models. Detailed explanation of the model equations can found3

elsewhere (4, 5, and 6). The inputs to the Witczak 1-37A model include mixture volumetrics,4

aggregate gradation, binder viscosity, and loading frequency. The mixture volumetrics include5

air voids and effective binder content. The gradation parameters include percent passing on the6

0.075 mm (No. 200) sieve, cumulative percent retained on the 19 mm (3/4 in.) sieve, cumulative7

percent retained on the 9.5 mm (3/8 in.) sieve, and cumulative percent retained on the 4.76 mm8

(No. 4) sieve. The inputs to the Witczak 1-40D model are similar to the inputs to the Witczak 1-9

37A model. The Witczak 1-40D model was intended to improve the Witczak 1-37A model and10

therefore, the binder viscosity and loading frequency parameters are replaced by the binder shear11

modulus |G*| and the binder phase angle. In this study, the binder frequencies at which |G*|12

measured were multiplied by a factor of 0.159 to calculate the mixture frequencies used in the13

Witczak 1-40D model. For the Hirsch model, the binder |G*|, voids in mineral aggregates, and14

voids filled with asphalt are incorporated. For this model, the loading frequency of the binder is15

the same as that for the mixture. These models were originally developed using HMA mixture16

material properties. The |E*| predictive capability of these models using plant produced WMA17

mixture data is presented below.18

1920

COMPARISON OF MEASURED AND PREDICTED |E*|21

Figure 6 presents the comparison of laboratory measured and predicted |E*| using the22

three models in arithmetic and logarithmic scales. A total of 570 data points were used involving23

only WMA mixtures tested at four temperatures and six loading frequencies. In order to meet24

one of the stated objectives, the control HMA mixtures (both plant produced and MATL mix25

design replication) were not included in the |E*| prediction analysis. In these figures, over-26

prediction of |E*| was observed when the Witczak 1-37A and 1-40D models were utilized. The27

over-prediction is pronounced with higher modulus values that correspond to the asphalt28

mixtures tested at high loading frequencies and low test temperatures. In the logarithmic scale,29

the Hirsch model predicted |E*| with the highest coefficient of determination (R2=0.9005) and30



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the lowest error (Se/Sy=0.3154) followed by the Witczak 1-40D model (R2=0.8453 and1

Se/Sy=0.3934) and the Witczak 1-37A model (R2=0.8074 and Se/Sy=0.4388). Better predictions2

were obtained using the Witczak 1-37A model following the Hirsch model when the arithmetic3

scale is considered. These findings are consistent with the model developers with high4

correlation coefficient and low error in logarithmic scale for the Witczak 1-40D and Hirsch5

models (5, 6). Comparisons of the predictive models amongst various WMA technologies (i.e.,6

foam, chemical, and organic) are also shown in Figure 7.7



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1

(a)2

3

(b)4

5

(c)6

FIGURE 6 Comparison of measured and predicted |E*| in arithmetic and logarithmic7

scales; (a) Witczak 1-37A, (b) Witczak 1-40D, and (c) Hirsch.8

0

5000

10000

15000

20000

25000

30000

35000

0 5000 10000 15000 20000 25000 30000 35000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.8106Se/Sy = 0.4352

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.8074Se/Sy = 0.4388

0

5000

10000

15000

20000

25000

30000

35000

0 5000 10000 15000 20000 25000 30000 35000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.5984Se/Sy = 0.6338

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.8453Se/Sy = 0.3934

0

5000

10000

15000

20000

25000

30000

35000

0 5000 10000 15000 20000 25000 30000 35000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.8854Se/Sy = 0.3386

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*|(MPa)

Measured |E*| (MPa)

R2 = 0.9005Se/Sy = 0.3154



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(a)1

(b)2

(c)3

FIGURE 7 |E*| Comparison of measured and predicted |E*| for various WMA4

technologies; (a) Witczak 1-37A, (b) Witczak 1-40D, and (c) Hirsch.5

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*

|(MPa)

Measured |E*| (MPa)

Foam

Chemical

Organic

Line of Equality

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*|(MPa)

Measured |E*| (MPa)

Foam

Chemical

Organic

Line of Equality

10

100

1000

10000

100000

10 100 1000 10000 100000

Predicted|E*|(MPa)

Measured |E*| (MPa)

Foam

Chemical

Organic

Line of Equality



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Accuracy of the |E*| Predictive Models1

The accuracy of the predictive models was determined by calculating the |E*| percent2

error (e) which equals the difference between predicted and measured |E*| divided by the3

predicted |E*|. For each test temperature and loading frequency, the |E*| percent error was4

computed and presented into seven groups: (a) e< 0%, (b) 0%


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(a) (b)1

2

(c) (d)3

FIGURE 8 Summary of predicted |E*| percent error (e); (a) 4.4C, (b) 21.1C, (c) 37.8C, and (d) 54.4C.4

0

20

40

60

80

e < 0% 0 < e 10% 10 < e 20% 20 < e 30% 30 < e 40% 40 < e 50% e > 50%

Percent(%)

Predicted |E*| Percent Error Range

Witczak 1-37A

Witczak 1-40D

Hirsch

0

20

40

60

80

e < 0% 0 < e 10% 10 < e 20% 20 < e 30% 30 < e 40% 40 < e 50% e > 50%

Percent(%)


Witczak 1-37A

Witczak 1-40D

Hirsch

0

20

40

60

80

e < 0% 0 < e 10% 10 < e 20% 20 < e 30% 30 < e 40% 40 < e 50% e > 50%

Percent(%)


Witczak 1-37A

Witczak 1-40D

Hirsch

0

20

40

60

80

e < 0% 0 < e 10% 10 < e 20% 20 < e 30% 30 < e 40% 40 < e 50% e > 50%

Percent(%)


Witczak 1-37A

Witczak 1-40D

Hirsch



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SUMMARY AND CONCLUSION1

This paper presents a comprehensive laboratory evaluation of WMA technologies2

stiffness properties and comparisons of three |E*| predictive models (Witczak 1-37A,3

Witczak 1-40D, and Hirsch). It included nine WMA demonstration projects; six foaming4

processes (Accu-Shear, Advera

, Aspha-min

, Aquablack

, Low Emission Asphalt (LEA),5

and Gencor); two chemical additives (Evotherm and Rediset

); and an organic additive6

(Sasobit). The rheological properties of the asphalt binders were characterized using the7

dynamic shear rheometer device at four test temperatures (4.4, 21.1, 37.8, and 54.4C) and8

multiple frequencies (0.016 to 25 Hz). The asphalt mixture performance tester was used to9

capture the stiffness properties of the asphalt mixtures using four temperatures (4.4, 21.1,10

37.8, and 54.4C) and six frequencies (25, 10, 5, 1, 0.5, and 0.1 Hz). The following11

conclusions can be drawn on the basis of the findings presented in this study:12

The Accu-Shearand Redisettechnologies measured slightly higher binder stiffness13as compared to their control binders primarily at low reduced frequency ranges. The14

LEA and Gencor technologies demonstrated higher binder stiffness as compared to the15

control binder at high reduced frequency ranges. The Advera

, Aspha-min, and16

Evothermtechnologies measured comparably similar binder stiffness values as their17

control binder at lower reduced frequency ranges.18

Compared to the control HMA mixtures, lower stiffness values were observed for the19WMA technologies prepared with foaming processes followed by the chemical20

additives. The reduction in stiffness is more pronounced for the asphalt mixtures21

containing Advera and Aspha-min

technologies. The WMA mixtures containing22

organic additive measured higher stiffness.23

The differences in the stiffness properties of the WMA technologies are attributed to,24among others, the differences in binder rheological properties, volumetric properties,25

WMA dosage rates, aggregate structure in the mix, production temperatures, and plant26

aging.27

Overall, reasonable |E*| predictions of the plant produced WMA technologies were28obtained when the Hirsch model was utilized followed by the Witczak 1-40D model29

and the Witczak 1-37A model.30

31



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RECOMMENDATIONS1

A comprehensive statistical analysis is needed to further investigate the effects of various2properties (e.g., volumetrics, binder, aggregate, WMA dosage rates, and aging) on3

binder/mixture stiffness performance.4

Refining the existing |E*| predictive models using WMA material data.5 Additional investigation into the AASHTOWare DARWin-ME predicted pavement6

distresses versus actual field WMA pavement distresses is required to determine if WMA7

pavement performance is similar to HMA.8

The dataset used in this paper can assist researchers and practitioners to calibrate and9validate the AASHTOWare DARWin-ME for designing new and rehabilitated WMA10

pavements.11

12

13

14

15

16

17

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19

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ACKNOWLEDGMENTS1

The success of this study is made possible through the close partnership of the transportation2

community. The FHWA Office of Pavement Technology wishes to express sincere thanks to3

the state Departments of Transportation (Colorado, Indiana, Louisiana, Minnesota, Missouri,4

Pennsylvania, Texas, and Wyoming) and the contractors involved in the projects. The5

authors would also like to acknowledge and extend special thanks to MATL programs6

mixture and binder laboratory technicians.7

8

9

10

11

12

13

14

15

16

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18

1920

21

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REFERENCES1

1. DAngelo, J., Harm, E., Bartoszek, J., Baumgardner, G., Corrigan, M., Cowsert, J.,2Harmon, T., Jamshidi, M., Jones, W., Newcombe, D., Prowell, B., Sines, R., and Yeaton,3

B. Warm-mix Asphalt: European Practice. Federal Highway Administration (FHWA),4

FHWA-PL-08-007,2008.5

2. Prowell, B., Hurley, G., and Frank, B. Warm-mix Asphalt: Best Practices. The National6Asphalt Pavement Association (NAPA), 2

ndEdition, Lanham, MD., Quality Improvement7

Series 125, 2011.8

3. Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures.9NCHRP 1-37A Project. Transportation Research Board of the National Academies,10

Washington, D.C., 2004.11

4. Andrei, D., Witczak, M. and Mirza, W. Appendix CC-4: Development of a Revised12Predictive Model for the Dynamic (Complex) Modulus of Asphalt Mixtures.13

Development of the 2002 Guide for the Design of New and Rehabilitated Pavement14

Structures, Final Document, NCHRP 1-37A, 1999.15

5. Bari, J. and Witczak, M. Development of a New Revised Version of the Witczak |E*|16Predictive Model for Hot Mix Asphalt Mixtures. In Journal of the Association of Asphalt17

Paving Technologists,Vol. 75, 2006, pp. 381-423.18

6. Christensen, D., Pellinen, T., and Bonaquist, R. Hirsch Model for Estimating the19Modulus of Asphalt Concrete. In Journal of the Association of Asphalt Paving20

Technologists,Vol. 72, 2003, pp. 97-121.21

7. Al-Khateeb, G., Shenoy, A. Gibson, N. and Harman, T. A New Simplistic Model for22Dynamic Modulus Predictions of Asphalt Paving Mixtures. InJournal of the Association23

of Asphalt Paving Technologists,Vol. 75, 2006, pp. 1254-1293.24

8. Ceylan, H., Gopalakrishnan, K., and Kim, S. Advanced Approaches to Hot-mix Asphalt25Dynamic Modulus Prediction. InCanadian Journal of Civil Engineering,Vol. 35, No. 7,26

2008, pp. 699-707.27

9. Sakhaeifar, M., Underwood, S., Ranjithan, R., Kim, R., and Jackson, N. Application of28Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete. In29

Transportation Research Record: Journal of the Transportation Research Board, No.30



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24

2127, Transportation Research Board of the National Academies, Washington, D.C.,1

2009, pp. 173-186.2

10.Dongre, R., Myers, L., DAngelo, J., Paugh, C. and Gudimettla, J. Field Evaluation of3Witczak and Hirsch Models for Predicting Dynamic Modulus of Hot-Mix Asphalt.4

Presented at 84th Annual Meeting of the Transportation Research Board, Washington,5

D.C., 2005.6

11.Azari, H., Al-Khateeb, G., Shenoy, A., and Gibson, N. Comparison of Simple7Performance Test |E*| of Accelerated Loading Facility Mixtures and Prediction |E*|: Use8

of NCHRP 1-37A and Witczaks New Equations. In Transportation Research Record:9

Journal of the Transportation Research Board,No. 1998, Transportation Research Board10

of the National Academies, Washington, D.C., 2007, pp. 1-9.11

12.Ceylan, H., Schwartz, C., Kim, S., and Gopalakrishnan, K. Accuracy of Predictive12Models for Dynamic Modulus of Hot-Mix Asphalt. Journal of Materials in Civil13

Engineering,Vol. 21, No. 6, 2009, pp. 286-293.14

13.Robbins, M., and Timm, D. Evaluation of Dynamic Modulus Predictive Equations for15NCAT Test Track Asphalt Mixtures. Presented at 90th Annual Meeting of the16

Transportation Research Board, Washington, D.C., 2011.17

14.Pellinen, T., Witczak, M., and Bonaquist, R. Asphalt Mix Master Curve Construction18Using Sigmoidal Fitting Function with Non-Linear Least Squares Optimization. In19

Recent Advances in Materials Characterization and Modeling of Pavement Systems,20

Geotechnical Special Publication, American Society of Civil Engineering,No. 123, 2004,21

pp. 83-101.22

Documents

1sk1bc-Comparative Evaluation of the Stiffness Properties of Warm-Mix Asphalt Technologies and E Predictive Models