Calibrating Mechanistic–Empirical Pavement Design Guide for North Carolina: Genetic Algorithm and Generalized Reduced Gradient Optimization Methods

Abstract

The Mechanistic–Empirical Pavement Design Guide (MEPDG) is the state-of-the-practice pavement analysis software developed under NCHRP Project 1–37A. Recently, AASHTO announced the first commercial version of the software, DARWin-ME, to replace the 1993 AASHTO design guide DARWin software. The MEPDG and DARWin-ME use similar models for predicting rutting and bottom-up fatigue cracking. Both distress models were nationally calibrated with measured performance data collected from hundreds of long-term pavement performance sections across the United States and Canada. Verification work indicated that these nationally calibrated models did not reflect North Carolina's local materials, construction practices, and local traffic. Therefore, the performance models must be recalibrated to reflect local conditions. The scope includes rutting and alligator cracking in flexible pavements. The development of rutting and fatigue model coefficients (k-values) is investigated for 12 commonly used hot-mix asphalt (HMA) mixtures in North Carolina, and two approaches for recalibrating the rutting and fatigue cracking model coefficients (β-factors) are compared to reflect local materials and conditions. The two optimization methods evaluated are generalized reduced gradient (GRG) and genetic algorithm (GA) optimization. Results indicate that rutting and fatigue cracking k-values for North Carolina HMA mixtures are generally different from national averages. The GA optimization method does a better job of predicting local distresses than do the GRG method and nationally calibrated models.