Approaches for local calibration of mechanistic-empirical pavement design guide joint faulting model: a case study of Ontario

ABSTRACT

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed by agencies as an innovative method for pavement design since the National Cooperative Highway Research Program (NCHRP) Project 1-37A was implemented in 2004. Over the years, the MEPDG has evolved into the AASHTOWare Pavement ME Design software (AASHTOWare®). Local calibration of the performance models in the AASHTOWare® is a crucial and challenging task to improve the effectiveness of its application. The accuracy of the calibration depends on efficient methods and validation processes. This paper aims at developing local calibration methods for joint faulting prediction model of Jointed Plain Concrete Pavement (JPCP). This study not only focuses on improving the prediction accuracy of the joint faulting model but also demonstrating the various optimisation procedures in detail. A total of 27 representative JPCP sections were used in the processes of calibration. Three optimisation approaches were used: (1) One-At-a-Time (OAT) through the trial-and-error procedure, (2) generalised reduced gradient (GRG) using MS Excel® Solver, and (3) Levenberg-Marquardt Algorithm (LMA) fitting the functions. The prediction accuracy of local models was improved as compared with the global ones. Average Bias (AB) reduced from 0.3083 to 0.0578, and Standard Error of the Estimate (SEE) reduced from 0.3345 to 0.1912. Among the three local calibration approaches, approach 2 and approach 3 had more significant improvement on results than approach 1. Finally, the integral procedures were provided for local calibration in Ontario.

KEYWORDS:

• Mechanistic-empirical pavement design guide
• local calibration
• optimization approach
• jointed plain concrete pavement
• joint faulting

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the China Scholarship Council (CSC): [Grant number 201606560029]; Intergovernmental International Cooperation on Science and Technology Key Innovation Project from the Ministry of Science and Technology of the People Republic of China: [Grant number 2016YFE0111000]; Highway Infrastructure Innovation Funding Program from the Ministry of Transportation of Ontario: [Grant number HIIFP2015] Henan Province Science and Technology Major Project from the Department of Science and Technology of Henan Province: [Grant number 151100310700].