Reliability-Based Mechanistic–Empirical Pavement Design with Statistical Methods

Abstract

“Reliability,” as defined in the Mechanistic–Empirical Pavement Design Guide (MEPDG), is an aggregated indicator defined as the probability that each of the key performance measures will be less than a selected critical level over the design period. Being such a complex system, the MEPDG—which is not a single closed-form design equation—cannot depend on classic reliability methods. Monte Carlo simulation is suitable for a robust reliability analysis but is impractical because of the extensive computation time required for any reasonable analysis with the MEPDG. The development of surrogate models for performance predictions becomes an option to represent the comprehensive modeling capability of the MEPDG efficiently. Improvements to the MEPDG reliability model based on several statistical methods are described. Five tasks are detailed. First, the MEPDG is calibrated to local conditions to reduce potential bias and variation from the national calibration. Then, risk analysis and screen analysis are conducted to determine the significant variables to include in surrogate models. Next, a comprehensive experimental design effectively plans MEPDG simulations. Then, response surface models of MEPDG are built through regression analysis. Finally, probabilistic design is achieved by Monte Carlo simulation. The result of these efforts is a state-specific MEPDG-based probabilistic pavement design tool kit named ReliME. This framework provides engineers more flexibility in data acquisition, design alternatives, optimization, and quality control. The framework's successful application in Arkansas could easily be used in other states and incorporated into future mechanistic–empirical pavement design software.