Calibration of Transverse Cracking and Joint Faulting Prediction Models in Pennsylvania for JPCP in AASHTOWare Pavement ME Design

The Pennsylvania Department of Transportation (PennDOT) recently conducted a study to implement and adopt the American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) procedure and accompanying software AASHTOWare Pavement ME Design (Pavement ME) into its routine pavement design practice. The implementation of the Pavement ME as a PennDOT standard required modifications in some aspects of PennDOT pavement design practices such as materials testing, testing equipment, traffic data reporting, software/database integration, development of statewide defaults for key inputs, policy regarding design output interpretation, and others. In addition, the implementation required local calibration of the software’s global pavement performance prediction models for Pennsylvania conditions to improve the accuracy of performance prediction for both asphalt concrete pavement and jointed plain concrete pavement (JPCP). This paper discusses the procedures and steps involved in locally calibrating the Pavement ME global transverse cracking and mean joint faulting prediction models for JPCP to account for Pennsylvania’s unique climate, traffic, and soils conditions, and its commonly used pavement construction practices. Local calibration of the Pavement ME models, using Pennsylvania input data, was performed using statistical analysis software’s nonlinear model optimization tool. The calibration efforts involved derivation of new local calibration factors for each performance prediction model using Pennsylvania-specific performance data. The outcome included elimination of bias (consistent over- or under-prediction), reduction of prediction error, and improvement in model prediction accuracy. The results indicate that compared to Pavement ME global performance prediction models, Pennsylvania-locally calibrated models could be used to develop more accurate, reliable, and cost-effective pavement designs.