Calibrating the Mechanistic-Empirical Pavement Design Guide for Kansas

The Kansas Department of Transportation (KDOT) is moving toward the implementation of the new American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) for pavement design. The MEPDG provides a rational pavement design framework based on mechanistic-empirical principles to characterize the effects of climate, traffic, and material properties on the pavement performance, as compared with the 1993 AASHTO Guide for Design of Pavement Structures. Before moving to the MEPDG, the nationally calibrated MEPDG distress prediction models need to be further validated and calibrated to the local condition. The objective of this research was to improve the accuracy of the MEPDG to predict the pavement performance in Kansas. This objective was achieved by evaluating the MEPDG-predicted performance of Kansas projects, as compared with the pavement performance data from the pavement management system (PMS), and calibrating the MEPDG models based on the pavement performance data. In this study, 28 flexible pavement projects and 32 rigid pavement projects with different material properties, traffic volumes, and climate conditions were strategically selected throughout Kansas. The AASHTO ME Design software (Version 1.3) was used in this study. The comparisons between the MEPDG-predicted pavement performance using the nationally calibrated models and the measured pavement performance indicated the need for the calibration of the MEPDG models to the Kansas conditions. For new flexible pavements, the MEPDG using the nationally calibrated models overestimated the rutting due to the overprediction of the deformation of the subgrade layer. Biases also existed between the predicted top-down cracking, thermal cracking, and International Roughness Index (IRI) and the measured data. The relationship between the measured and the predicted IRIs was more obvious than that for the cracking. Using the coefficients determined through local calibration in this study, the biases and the standard errors were minimized for all the models based on the statistical analysis. For new rigid pavements, very low mean joint faulting was measured in actual projects as compared with the default threshold of the MEPDG. The type of base course had a minor effect on the pavement performance. The traditional splitting data method was adopted in the process of local calibration. After the local calibration, the biases between the predicted pavement performance (mean joint faulting and IRI) and the measured pavement performance were minimized, and the standard errors were reduced.

[1]  Jagannath Mallela,et al.  Guidelines for Implementing NCHRP 1-37A M-E Design Procedures in Ohio: Volume 4—MEPDG Models Validation & Recalibration , 2009 .

[2]  W O Hadley,et al.  PACIFIC RIM TRANSTECH CONFERENCE PROCEEDINGS. VOLUME II. CHANGES TO THE AASHTO GUIDE FOR DESIGN OF PAVEMENT STRUCTURES , 1993 .

[3]  Fadi Munir Jadoun.  Calibration of the Flexible Pavement Distress Prediction Models in the Mechanistic Empirical Pavement Design Guide (MEPDG) for North Carolina , 2011 .

[4]  Iliya Yut,et al.  Implementation of the MEPDG for New and Rehabilitated Pavement Structures for Design of Concrete and Asphalt Pavements in Minnesota , 2009 .

[5]  Y. Richard Kim,et al.  Local Calibration of the MEPDG for Flexible Pavement Design , 2011 .

[6]  Harold L Von Quintus,et al.  Mechanistic-Empirical Pavement Design Guide Flexible Pavement Performance Prediction Models for Montana--Volume I Executive Research Summary , 2007 .

[7]  Ghassan R. Chehab,et al.  Implementing the Mechanistic-Empirical Design Guide Procedure for a Hot-Mix Asphalt-Rehabilitated Pavement in Indiana , 2005 .

[8]  Danny X Xiao,et al.  Calibration of the Mechanistic–Empirical Pavement Design Guidefor Flexible Pavement Design in Arkansas , 2011 .

[9]  E. W. C. Wilkins,et al.  Cumulative damage in fatigue , 1956 .

[10]  Marcelo Bustos,et al.  Calibration of Distress Models from the Mechanistic–Empirical Pavement Design Guide for Rigid Pavement Design in Argentina , 2011 .

[11]  Magdy Abdelrahman,et al.  Improving prediction accuracy in mechanistic-empirical pavement design guide , 2006 .

[12]  Ambarish Banerjee,et al.  Regional Calibration of Permanent Deformation Performance Models for Rehabilitated Flexible Pavements , 2011 .

[13]  Ciro Caliendo.  Local Calibration and Implementation of the Mechanistic-Empirical Pavement Design Guide for Flexible Pavement Design , 2012 .

[14]  Linda M Pierce,et al.  Calibration of NCHRP 1-37A software for the Washington state department of transportation : Rigid pavement portion , 2006 .

[15]  Jagannath Mallela,et al.  Implementation of the AASHTO Mechanistic-Empirical Pavement Design Guide for Colorado , 2013 .

[16]  Harold L Von Quintus,et al.  Implementation of the Mechanistic-Empirical Pavement Design Guide in Utah: Validation, Calibration, and Development of the UDOT MEPDG User’s Guide , 2009 .

[17]  Y. Richard Kim,et al.  Comparison of Flexible Pavement Distresses Monitored by North Carolina Department of Transportation and Long-Term Pavement Performance Program , 2010 .

[18]  Iliya Yut,et al.  Adaptation of Mechanistic-Empirical Pavement Design Guide for Design of Minnesota Low-Volume Portland Cement Concrete Pavements , 2008 .

[19]  Linda M Pierce,et al.  Implementation of the AASHTO Mechanistic-Empirical Pavement Design Guide and Software , 2014 .

[20]  Michael S Mamlouk,et al.  Necessary Assessment of Use of State Pavement Management System Data in Mechanistic–Empirical Pavement Design Guide Calibration Process , 2010 .

[21]  Rodrigo Delgadillo,et al.  Toward Implementation of the Mechanistic–Empirical Pavement Design Guide in Latin America , 2011 .

[22]  Kyle Hoegh,et al.  Local Calibration of Mechanistic–Empirical Pavement Design Guide Rutting Model , 2010 .

[23]  José Pablo Aguiar-Moya,et al.  Calibration of Mechanistic–Empirical Pavement Design Guide Permanent Deformation Models , 2009 .

[24]  David R Luhr,et al.  Pavement Performance Modeling Using Piecewise Approximation , 2010 .

[25]  Youngsoo Kim,et al.  Local Calibration of Mechanistic-Empirical Pavement Design Guide for Flexible Pavement Design , 2008 .

[26]  Halil Ceylan,et al.  Iowa Calibration of MEPDG Performance Prediction Models , 2013 .

[27]  José Pablo Aguiar-Moya,et al.  Calibrating the MEPDG Permanent Deformation Performance Model for Different Maintenance and Rehabilitation Strategies , 2010 .

[28]  Teresa M Adams,et al.  Development of a Regional Pavement Performance Database for the AASHTO Mechanistic-Empirical Pavement Design Guide: Part 2: Validations and Local Calibration , 2007 .

[29]  Xiaoming Yang,et al.  Evaluation of Current Louisiana Flexible Pavement Structures Using PMS Data and New Mechanistic- Empirical Pavement Design Guide , 2012 .

[30]  Linda M Pierce,et al.  Calibration of Flexible Pavement in Mechanistic–Empirical Pavement Design Guide for Washington State , 2009 .

[31]  Mohamed M El-Basyouny,et al.  Calibration of the AASHTO MEPDG for Flexible Pavement for Arizona Conditions , 2010 .

[32]  Halil Ceylan,et al.  Use of Pavement Management Information System for Verification of Mechanistic–Empirical Pavement Design Guide Performance Predictions , 2010 .