calibration of mepdg for flexible pavement design …docs.trb.org/prp/11-3562.pdf2 hall, xiao and...

Calibration of the MEPDG for Flexible Pavement Design in 1 Arkansas 2

3 4

Word Count: 4114 (Manuscript: 3064; 2 Tables and 5 Figures: 1050) 5 6 7 8 9 10 11

Authors: 12 13

Kevin D. Hall (corresponding author) 14 Professor and Head 15

Department of Civil Engineering 16 University of Arkansas 17

4190 Bell Engineering Center, Fayetteville, AR 72701 18 (479) 575-8695 19

[email protected] 20 21

Danny X. Xiao 22 Graduate Research Assistant 23

Department of Civil Engineering 24 University of Arkansas 25

4190 Bell Engineering Center, Fayetteville, AR 72701 26 (479)799-7686 27 [email protected] 28

29 Kelvin C.P. Wang 30

Professor 31 Department of Civil Engineering 32

University of Arkansas 33 4190 Bell Engineering Center, Fayetteville, AR 72701 34

479-575-8425 35 [email protected] 36

37 38 39 40 41 42

Paper Prepared for Publication and Presentation at the 43 2011 Annual Meeting of the Transportation Research Board 44

45 November, 2010 46

47

TRB 2011 Annual Meeting Paper revised from original submittal.

1 Hall, Xiao and Wang

ABSTRACT 1 The Mechanistic-Empirical Pavement Design Guide (MEPDG) contains pavement 2 performance prediction models calibrated primarily using data from the Long-Term 3 Pavement Performance (LTPP) program. Because of potential differences between ‘national’ 4 and ‘local’ conditions states are reporting either a partial or full calibration of the MEPDG on 5 a local level. A national guideline for local calibration was developed under NCHRP Project 6 1-40B. Arkansas has invested heavily in efforts to implement the MEPDG. The initial local 7 calibration of flexible pavement models in the MEPDG for Arkansas is summarized. For the 8 current MEPDG, predicted distresses did not accurately reflect measured distresses, 9 particularly for longitudinal and transverse cracking. However, the pavement sections 10 available for this study are generally in good condition. Due to the lack of measured 11 transverse cracking, the transverse cracking model was not calibrated. The difference in 12 defining transverse cracking between the MEPDG and LTPP may be critical in terms of data 13 collection and identification. Thermal cracking should be specifically identified in a 14 transverse cracking survey to calibrate the transverse cracking model in MEPDG. 15 Calibration coefficients were optimized for the alligator cracking and longitudinal cracking 16 models. In general the alligator cracking and longitudinal cracking models are improved by 17 calibration. However a question remains regarding the suitability of the calibrated models for 18 routine design. In addition, the smoothness model (IRI) was not calibrated, since the 19 predicted IRI is a function of the other predicted distresses. A lack of data forced the use of 20 many default values in the MEPDG. It is recommended that additional sites in Arkansas be 21 established and a more robust data collection procedure be implemented for future calibration 22 efforts. The procedure for local calibration of the MEPDG using LTPP and PMS data in 23 Arkansas is established. Additional development of database software for data manipulation, 24 pre-processing, and quality control – currently underway in Arkansas – will significantly 25 streamline the calibration process. 26 27

28



INTRODUCTION 1 The Mechanistic-Empirical Pavement Design Guide (MEPDG) was produced in 2004 2 through National Cooperative Highway Research Program (NCHRP) Project 1-37A, and 3 subsequently delivered to the American Association of State Highway and Transportation 4 Officials (AASHTO) in 2008. Pavement performance prediction models contained in the 5 current MEPDG were calibrated primarily using data from the Long-Term Pavement 6 Performance (LTPP) program. Because of potential differences between ‘national’ and ‘local’ 7 conditions – including climate, material properties, traffic patterns, construction and 8 maintenance activities – pavement performance predicted by the MEPDG should be 9 compared to and verified against local experience. Moreover, LTPP data from sites located in 10 some states (e.g. Arkansas) were not used in the national calibration; local calibration is 11 likely necessary for these locations. 12 13 States are reporting either a partial or full calibration of the MEPDG on a local level. Kang 14 and Adams (2007) calibrated the longitudinal and alligator fatigue cracking models for 15 Michigan, Ohio and Wisconsin. All models except top-down longitudinal cracking model 16 were validated for Montana (Von Quintus and Moulthrop, 2007). It was found that the 17 MEPDG over predicted total rutting because significant rutting was predicted in unbound 18 base and subgrade soil. Muthadi and Kim (2008) calibrated the rutting and bottom-up fatigue 19 cracking model for North Carolina using a spreadsheet-based approach. In an overview of 20 selected calibration studies, Von Quintus (2008) found that the measurement error of the 21 performance data has the greatest effect on the precision of MEPDG models. California 22 utilized data from accelerated pavement testing (APT) to calibrate its mechanistic empirical 23 pavement models (Ullidtz et al., 2008). Although data from APT could be ideal for model 24 calibration considering its advantages of controlled climate condition, precise loading, and 25 testing until pavement fails, most of states that do not have APT facilities can only rely on in-26 service pavement sites. Texas was divided into five regions for the calibration of rutting 27 models (Banerjee, Aguiar-Moya and Prozzi, 2009). Washington selected two representative 28 calibration sections to calibrate all distress models (Li, Pierce and Uhlmeyer, 2009). A 29 national guideline for local calibration was also developed by NCHRP Project 1-40B (Von 30 Quintus, Darter and Mallela, 2009). Using Pavement Management Information System 31 (PMIS), MEPDG were verified for Iowa (Kim et al. 2010). Systematic difference was found 32 for rutting and cracking models. 33 34 Arkansas has invested heavily in efforts to implement the MEPDG. An initial sensitivity 35 analysis was conducted to determine the most significant parameters of the MEPDG (Hall 36 and Beam, 2005). Selected primary inputs required by the MEPDG, but not available through 37 traditional testing, were then analyzed – including hot-mix asphalt (HMA) dynamic modulus, 38 various aspects of the traffic load spectra, and the coefficient of thermal expansion of 39 Portland cement concrete (PCC) (Tran and Hall, 2007). In addition, a project aimed to 40 manage all data for the MEPDG was completed in which software, termed ‘PrepME’, was 41 developed to conveniently prepare data sets for MEPDG use (Wang et al, 2009). Currently, a 42 local calibration effort is progressing to allow the routine use of the MEPDG in Arkansas. 43 44



This paper presents the process of local calibration, including data retrieval, data quality 1 checks, validation, calibration, and verification. Problems and issues encountered during the 2 process are highlighted. 3 4 DATA PREPARATION 5 The MEPDG differs from many traditional pavement design methods in that the MEPDG 6 requires substantially more data related to climate, traffic, and materials. In Arkansas, two 7 sources are available for data necessary for local calibration: the Long-Term Pavement 8 Performance (LTPP) database and the Pavement Management System (PMS) maintained by 9 the Arkansas State Highway and Transportation Department (AHTD). Unfortunately, 10 Arkansas has relatively few LTPP sections. For flexible pavements, General Pavement 11 Studies (GPS) sites GPS-1 and GPS-2, and Specific Pavement Studies (SPS) sites SPS-1 and 12 SPS-8 may be used. The AHTD PMS contains pavement materials, construction, and 13 performance data for a variety of sites, focused primarily on sites constructed since 1996 14 when the Superpave HMA mixture design system was implemented in Arkansas. 15 16 Table 1 lists 26 sections available from both LTPP and PMS sources, categorized by HMA 17 thickness and base types. Eighty percent of the sections (20 sites) were randomly selected for 18 calibration efforts; twenty percent (6 sites) are preserved for subsequent validation. 19 20

TABLE 1 Experiment Matrixa,c 21

HMA thickness ACb+GBb AC+ATBb AC+CTBb

No. of sections

Thin (≤5 in.) 0113, 0803 0116, 0118, 0120, 0121, 0122 7

Intermediate 0114, 0804, 070079, R50084

0115, 0117, 0119, 0123, 0124, 3058, 3071, R80065, R50067

2042, 3048 15

Thick (≥8 in.) R20149, 090001, 009948, 070018 4

No. of sections 10 14 2 26 aNumbers in the table refer to job/project numbers for various asphalt pavements in Arkansas 22 bAC: Asphalt Concrete; GB: Granular Base; ATB: Asphalt Treated Base; CTB: Cement Treated Base; 23 PCC: Portland Cement Concrete. 24 cGray (highlighted) pavement sections randomly selected for validation. 25 26 Figure 1 shows the locations of these sections. They are well distributed across the five 27 physiographic regions of Arkansas: Ozark plateaus, Arkansas River valley, Ouachita 28 Mountains, West gulf coastal plain, and the Mississippi river alluvial plain. 29 30 Data for the LTPP sections was obtained from the latest LTPP Standard Data Release 24, 31 which was released in January 2010. Data for the AHTD sites was taken from the PMS 32 database. MEPDG Version 1.100 was used to generate pavement performance predictions in 33 this study. 34 35 Traffic 36 37



The LTPP database contains sufficient traffic data, such as volume count, vehicle 1 classification and axle load distribution, to be used directly in the MEPDG. However, only 2 volume count and truck percentage are available from the Arkansas PMS. Therefore, results 3 from previous research were used to provide consistent data for all sections (Tran & Hall, 4 2007). Default values were used for monthly adjustment, hourly truck distribution, and 5 general traffic input (Level 3 input). Site specific vehicle class distribution data was used 6 whenever it was available (Level 1 input); otherwise, recommended values from MEPDG 7 were used according to Truck Traffic Classification (TTC) groups (Level 2 input). Statewide 8 axle load distribution factors from previous research were used in this study (Level 2 input). 9 10 The 26 sites cover different levels of traffic. The Average Annual Daily Truck Traffic 11 (AADTT) ranges from 10 to 10,475 vehicles per day. Traffic growth rate ranges from 0 to 12 6.9%. In terms of functional classification, these sites include rural interstates, rural major 13 arterials, minor arterials and major collectors. 14 15

16 FIGURE 1 Locations of calibration sites. 17

18 Climate 19 20

PMS

LTPP

05-3071 090001

009948

05-0113~0124

05-3058

R50067

R50084

R80065

05-3048 05-0803,0804

R20149

05-2042

070079

070018



By providing the GPS coordinates of each site, climate data was generated by interpolating 1 from nearby climate stations. Depth of water table was extracted from the National Water 2 Information System of the United States Geological Survey (USGS). Note that this is only a 3 rough estimation because sites with valid well-data may not be close to the pavement site. 4 5 Structure 6 7 Layer structures of LTPP sites are recorded in Section_Layer_Structure in 8 Administration.mdb. Information regarding HMA mixtures, such as gradation, binder type, 9 and volumetric properties are available in Inventory.mdb and Material_Test.mdb. Default 10 values were accepted for thermal properties. It should be noted that the as-built air voids was 11 assumed to be 8 percent for all sections according to Arkansas construction specifications 12 (Arkansas State Highway and Transportation Department, 2003). Only a limited amount of 13 information is available for base and subgrade properties, such as gradation, plasticity index, 14 liquid limit and stiffness/modulus. Therefore, MEPDG Level 3 default values were accepted 15 as long as the material type is accurately determined. 16 17 Performance Data 18 19 Five flexible pavement performance predictions are provided by the MEPDG: alligator 20 cracking, longitudinal cracking, transverse cracking, rutting, and International Roughness 21 Index (IRI). For LTPP sections, the corresponding measured performance data are recorded 22 in Monitoring.mdb. Similar to the national calibration (ARA, 2004), low, medium and high 23 severity alligator cracking were summed as ‘alligator cracking’ without adjustment; low, 24 medium and high severity in-wheelpath longitudinal cracking were added without adjustment 25 as ‘longitudinal cracking’; and low, medium and high severity transverse cracking were 26 summed as ‘transverse cracking’ using the same weighting function in the national 27 calibration. 28 29 Sections in the Arkansas PMS have hard-copy records of yearly manual distress surveys, 30 rutting measurements using straightedge method and some profile measurements. The LTPP 31 Distress Identification Manual was followed in all the manual distress surveys (Miller and 32 Bellinger, 2003). The records were interpreted manually according to the distresses listed in 33 LTPP database. Therefore, the performance data of LTPP sections and PMS sections are 34 somewhat comparable. However, a concern was noted. Crack length, as recorded on hard-35 copy forms in the PMS, have been found to vary from the actual distance in the field. This 36 may be exacerbated by the shortened length of the PMS sections (100 ft) as compared to 37 LTPP sections (500 ft). A small error in the hard-copy forms may become significant when 38 extrapolated to feet-per-mile. 39 40 RESULTS ANALYSIS 41 42 Verification 43 44 After data preparation, the MEPDG was run with the national-default calibration coefficients. 45 Figure 2 shows the comparison of predicted and measured alligator cracking, longitudinal 46



cracking, transverse cracking, rutting and IRI. Figure 3 illustrates the difference between 1 predicted and actual distress as a function of HMA thickness, for longitudinal cracking and 2 rutting. All data is presented here; there is no attempt in this analysis to identify and remove 3 ‘outliers’ from the data sets. 4 5 It is observed that predicted distresses do not match well with measured distresses, 6 particularly for longitudinal and transverse cracking. However, it should be pointed out that 7 most of the data points group near the origin in Figures 2 (a), (b) and (c). For example 91 8 percent of measured alligator cracking is lower than 10 percent; in addition, 92 percent of 9 predicted longitudinal cracking is lower than 1000 ft/mi. In general, the pavement sections 10 available for this study are in good condition (on average, only 3.1% alligator cracking, 711 11 ft/mi longitudinal cracking, 126 ft/mi transverse cracking, 0.21 inches total rutting, and 70.9 12 in/mi for IRI). Additional observations related to the results follow. 13

• Fatigue Cracking: Both alligator cracking and longitudinal cracking predicted by 14 MEPDG are forms of fatigue cracking. Transfer functions are used to predict visual 15 cracking from mechanistic “damage” at the bottom and top of HMA layers. This 16 results in the HMA layer thickness becoming a significant factor influencing 17 performance predictions. 18

• Asphalt Treated Base (ATB): Although it is a type of base, ATB is not modeled as 19 “Treated Base” but as “Asphalt” (albeit with a reduced stiffness). Therefore, the 20 HMA layer in the sections with asphalt treated base becomes very thick in the 21 MEPDG, which reduces the stress and strain at the bottom and top of the HMA layer, 22 in turn reducing the predicted alligator cracking and longitudinal cracking. 23

• Transverse Cracking: In the MEPDG, transverse cracking is primarily related to 24 thermal cracking, caused by thermal stress in pavement. However, transverse 25 cracking in LTPP database and PMS are measured according to the LTPP Distress 26 Identification Manual, in which transverse cracking is defined as cracks that are 27 predominately perpendicular to pavement centerline (Miller & Bellinger, 2003). The 28 implementation of Performance-Graded (PG) binders for HMA in Arkansas has all-29 but eliminated thermal cracking in flexible pavements; accordingly the MEPDG 30 predicts no thermal cracking for Arkansas climate, using a properly selected PG 31 binder. However, transverse cracking is recorded in distress surveys, suggesting 32 additional cracking mechanisms may be predominate in Arkansas. 33

• Rutting: Eighty percent of the pavement sections have 0.1 to 0.3 inches of rutting, 34 even for the sites older than 15 years. This suggests either: (a) rutting reached a 35 maximum of 0.3 inches by consolidation under traffic, without plastic failure; or (b) 36 rutting measurements (typically by straightedge) were recorded as a maximum of 0.3 37 inches regardless of the actual measurement. 38

39



1 (a) (b) 2

3 (c) (d) 4

5 (e) 6

FIGURE 2 Verification of national calibrated model: (a) alligator cracking, (b) 7 longitudinal cracking, (c) transverse cracking, (d) total rutting, and (e) IRI. 8

0

5

10

15

20

25

30

35

40

0 10 20 30 40

Pred

icte

d A

lliga

tor C

rack

ing

(%)

Measured Alligator Cracking (%)

N=301

0

1000

2000

3000

4000

5000

6000

7000

0 2000 4000 6000Pred

icte

d Lo

ngitu

dina

l Cra

ckin

g (f

t/mi)

Measured Longitudinal Cracking (ft/mi)

N=301

0

0.2

0.4

0.6

0.8

1

0 1000 2000 3000

Pred

icte

d Tr

ansv

erse

Cra

ckin

g (f

t/mi)

Measured Transverse Cracking (ft/mi)

N=301

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0 0.2 0.4 0.6 0.8

Pred

icte

d To

tal R

uttin

g (in

)

Measured Total Rutting (in)

N=293

0

20

40

60

80

100

120

140

0 30 60 90 120

Pred

icte

d IR

I (in

/mi)

Measured IRI (in/mi)

N=193



1

2 (a) (b) 3

FIGURE 3 HMA thickness influences the prediction of (a) longitudinal cracking, 4 and (b) total rutting. (Note: Residual = Predicted – Measured) 5

6 7 Calibration 8 9 Generally, prediction models are calibrated by minimizing the sum of standard error (SSE) 10 between predicted and measured values: 11

∑=

−=N

imeasuredpredictedSSE

1

2)( (1)

12

Due to the nature of the data, the transverse cracking model was not calibrated. In addition, 13 the smoothness model (IRI) was not calibrated, since the predicted IRI is a function of other 14 predicted distresses. The Solver function within Microsoft Excel was used to optimize the 15 coefficients in the alligator cracking and longitudinal cracking models. Iterative runs of the 16 MEPDG using discrete calibration coefficients were utilized to optimize the rutting model. 17 For this analysis, it was assumed that the national rutting model for granular base is the same 18 as it for Arkansas because rutting mainly occurs in the HMA layers and subgrade; hence, the 19 default coefficient for rutting in granular base was not adjusted. Table 2 lists the adjusted 20 calibration coefficients. Figure 4 provides a comparison of the predicted and measured 21 distresses before and after calibration. 22 23 Observations based on the calibration process and results follow. 24

• General: the alligator cracking and longitudinal cracking models are improved by 25 calibration. However a question remains regarding the suitability of the calibrated 26 models for routine design. 27

• Quality of Input Data: Many default values are used in MEPDG in this study because 28 these data are not available. There is a continuing concern that the quality of input 29 data reduces the accuracy of MEPDG. It is recommended that additional sites be 30 established and a more robust data collection procedure be implemented for future 31 calibration efforts. 32

33

y = -67.775x + 438.5R² = 0.0245

-8000-6000-4000-2000

02000400060008000

10000

0 5 10 15 20 25

Res

idua

l of L

C (f

t/mi)

HMA Thickness (in)

N=301

y = -0.0172x + 0.2893R² = 0.282

-0.3-0.2-0.10.00.10.20.30.40.50.60.7

0 5 10 15 20 25

Res

idua

l of

Tota

l Rut

ting

(in)

HMA Thickness (in)

N=293



TABLE 2 Summary of Calibration Factors 1

Calibration Factor Default Calibrated Alligator cracking

C1 1 0.688 C2 1 0.294 C3 6000 6000

Longitudinal cracking C1 7 3.016 C2 3.5 0.216 C3 0 0 C4 1000 1000

AC rutting βr1 1 1.20 βr2 1 1 βr3 1 0.80

Base rutting Bs1 1 1

Subgrade rutting Bs1 1 0.50

2

3 (a) (b) 4

5

0

5

10

15

20

25

0 10 20Pred

icte

d A

lliga

tor C

rack

ing

(%)


N=229

0

5

10

15

20

25

0 5 10 15 20 25Pred

icte

d A

lliga

tor C

rack

ing

(%)


N=229

0500

100015002000250030003500400045005000

0 1000 2000 3000 4000 5000

Pred

icte

d Lo

ng.l

Cra

ckin

g (f

t/mi)


N=225

0500

100015002000250030003500400045005000

0 1000 2000 3000 4000 5000

Pred

icte

d Lo

ng. C

rack

ing

(ft/m

i)


N=225



(c) (d) 1

2 (e) (f) 3

FIGURE 4 Comparison of national (a, c, e) and calibrated (b, d, f) models. 4

5 Validation 6 7 The calibrated models are validated by running the MEPDG on the remaining six sites to 8 compare predicted and measured performance. One example (site 05-0113) is shown in 9 Figure 5. The five additional sites demonstrate similar results. It is clear that local calibration 10 reduced the difference between predicted and measured distress; additional efforts (sites, data) 11 will be necessary to further reduce this difference. 12 13

14 FIGURE 5 Comparison of national and calibrated models on site 05-0113. 15

16 CONCLUSIONS 17 This paper summarizes the initial local calibration of flexible pavement models in the 18 MEPDG for the Arkansas. Conclusions from the effort follow. 19

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.2 0.4 0.6

Pred

icte

d To

tal R

uttin

g (in

)


N=2250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.2 0.4 0.6

Pred

icte

d To

tal R

uttin

g (in

)


N=225

0

10

20

30

40

50

60

70

0 50 100 150 200 250

Alli

gato

r Cra

ckin

g, %

Month

NationalLocalMeasurementLimit

00.10.20.30.40.50.60.70.80.9

1

0 50 100 150 200 250

Tota

l Rut

ting,

in

Month

NationalLocalMeasurementLimit



• The availability and quality of design, materials, construction, and performance data 1 are critical for local calibration. It is likely that states like Arkansas will need to 2 establish additional calibration sites to supplement available LTPP and PMS data. 3

• The difference in defining transverse cracking between the MEPDG and LTPP may 4 be critical in terms of data collection and identification. Thermal cracking should be 5 specifically identified in a transverse cracking survey to calibrate the transverse 6 cracking model in MEPDG. 7

• Proper modeling of asphalt treated base is vital to producing realistic predictions of 8 alligator cracking and longitudinal cracking, due to the influence of total HMA 9 thickness on the damage predictions at the bottom and top of HMA layer. 10

• The procedure for local calibration of the MEPDG using LTPP and PMS data in 11 Arkansas is established. Additional development of database software for data 12 manipulation, pre-processing, and quality control – currently underway in Arkansas – 13 will significantly streamline the calibration process. 14

15 ACKNOWLEDGEMENTS 16 This paper was prepared under research project TRC-1003, “Local Calibration of MEPDG”, 17 sponsored by the Arkansas State Highway and Transportation Department and the Federal 18 Highway Administration. The author would like to thank Daniel Byram, Jacqueline Hou for 19 their work on this project. The opinions expressed in the paper are those of the authors, who 20 are responsible for the accuracy of the facts and data herein, and do not necessarily reflect the 21 official policies of the sponsoring agencies. This paper does not constitute a standard, 22 regulation, or specification. 23 24 REFERECNES 25 ARA Inc. (2004). Guide. Retrieved 4 15, 2009, from Mechanistic-Empirical Pavement 26

Design Guide: http://www.trb.org/mepdg/guide.htm 27 Arkansas State Highway and Transportation Department. (2003). Arkansas 2003 Standard 28

Specification for Highway Construction. Little Rock: Arkansas State Highway and 29 Transportation Department. 30

Banerjee, A., Aguiar-Moya, J., & Prozzi, J. A. (2009). Texas experience using LTPP for 31 calibration of the MEPDG permanent deformation models. Transportation Research 32 Record: Journal of the Transportation Research Board, No. 2094 , 12-20. 33

Hall, K. D., & Beam, S. (2005). Estimating the sensitivity of design input variables for rigid 34 pavement analysis with a mechanistic-empirical design guide . Transportation 35 Research Record: Journal of the Transportation Research Board , 65-73. 36

Kang, M., & Adams, T. M. (2007). Local calibration for fatigue cracking models used in the 37 Mechanistic-Empirical Pavement Design Guide. Proceedings of the 2007 Mid-38 Continent Transportation Research Symposium. Ames, Iowa. 39

Kim, S., Ceylan, H., Gopalakrishnan, K., Smadi, O., Brakke, C., & Behnami, F. (2010). 40 Verification of Mechanistic-Empirical Pavement Design Guide (MEPDG) 41 Performance Predictions Using Pavement Management Information System (PMIS). 42 Transportation Research Board Annual Meeting (pp. CD-ROM). Washington D.C.: 43 Transportation Research Board. 44



Li, J., Pierce, L. M., & Uhlmeyer, J. (2009). Calibration of Flexible Pavement in 1 Mechanistic-Empirical Pavement Design Guide fr Washington State. Transportation 2 Research Record: Journal of the Transportation Research Board, No. 2095 , 73-83. 3

Miller, J. S., & Bellinger, W. Y. (2003). Distress Identification Manual for the Long-Term 4 Pavement Performance Program . Washington D.C.: Federal Highway 5 Administration. 6

Muthadi, N. R., & Kim, Y. R. (2008). Local Calibration of Mechanistic-Empirical Pavement 7 Design Guide for Flexible Pavement Design. Transportation Research Record: 8 Journal of the Transportation Research Board, No. 2087 , 131-141. 9

Tran, N. H. (2005). Characterizing and predicting dynamic modulus of hot-mix asphalt for 10 mechanistic-empirical design guide. Fayetteville, AR: University of Arkansas. 11

Tran, N., & Hall, K. D. (2007). Development and influence of statewide axle load spectra an 12 flexible pavement performance. Transportation Research Record: Journal of the 13 Transportation Research Board, No. 2037 , 106-114. 14

Ullidtz, P., Harvey, J., Tsai, B.-W., & Monismith, C. L. (2008). Calibration of Mechanistic-15 Empirical Models for Flexible Pavements Using California Heavy Vehicle 16 Simulators. Transportation Research Record: Journal of the Transportation Research 17 Board , 20-28. 18

Von Quintus, H. (2008). Local Calibration of MEPDG- An Overview of Selected Studies. 19 Journal of the Association of Asphalt Paving Technologists , 935-974. 20

Von Quintus, H. L., Darter, M., & Mallela, J. (2009). Recommended Practice for Local 21 Calibration of the ME Pavement Design Guide. Round Rock, Texas: Applied 22 Research Associates, Inc.-Transportation Sector. 23

Von Quintus, H., & Moulthrop, J. (2007). Mechanistic-Empirical Pavement Design Guide 24 Flexible Pavement Performance Prediction Models. Helena, MT: Montana 25 Department of Transportation. 26

Wang, K. C., Li, Q., Hall, K. D., Nguyen, V., Gong, W., & Hou, Z. (2008). Database Support 27 for the New Mechanistic-Empirical Pavement Design Guide . Transportation 28 Research Record: Journal of the Transportation Research Board, No. 2087 , 109-29 119. 30

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