Evaluation of Laboratory, Construction, and Performance Variability by Bootstrapping and Monte Carlo Methods for Rutting Performance Prediction of Heavy Vehicle Simulator Test Sections

Abstract

This paper demonstrates an innovative reliability analysis approach for prediction of asphalt rutting performance. In this approach, reliability was evaluated by considering the variability in laboratory test results, layer thicknesses, stiffnesses, and measured in situ performance. The effects of input design parameters variability on predicted performance were determined using the calculated distributions of calibration coefficients. To assess the contribution of each input parameter’s precision to the precision of calculated calibration coefficients, various cases were created by including and excluding the variability in these parameters in the calibration process. These distributions were also used for rutting performance prediction and reliability evaluation of highway sections. In this way, rut depths for different reliability levels can be predicted without performing computationally intensive calculations within the design software. The results indicated that distributions of calibration coeffici...