Regional Sensitivity Analysis of the M-E Flexible Pavement Design Using the Monte Carlo Filtering Method

Abstract

The new Mechanistic-Empirical Pavement Design Guide (MEPDG) requires a large number of design input parameters. For a design agency, it is rational to focus on data collection of input parameters that are more influential to the design output. Sensitivity analyses help to identify these important input parameters. In the past, both local and global sensitivity analyses have been carried out. Different significance indicators have been used to rank the importance of the design input parameters. However, both local and global sensitivity analyses have limitations. In this study, an example of a regional sensitivity analysis (RSA) conducted on the new MEPDG design software (DARWin-ME) using the Monte Carlo filtering (MCF) method was presented. As demonstrated in this example, the presented RSA method is advantageous in identifying input parameters that are most influential to the designed pavement thickness using the MEPDG. BACKGROUND DARWin-ME is the new generation AASHTOWare® pavement design software developed based on the new mechanistic-empirical pavement design guide (MEPDG). Compared to the previous AASHTO pavement design guide, the new MEPDG requires a large number of design inputs to characterize traffic, climate, pavement structure, and materials. For a highway design agency, it is rational to focus the data collection effort to input parameters that are more influential to the design output. These influential input parameters can be identified from sensitivity analysis. Sensitivity analysis is a statistics procedure to evaluate the variability of the output of a model due to the change of the input. In the past, many researchers have conducted sensitivity analyses on the MEPDG. Different sensitivity analysis techniques often resulted in different results. There is an urgent need to develop a standardized sensitivity analysis procedure for the MEPDG. The objective of this paper is to introduce a regional sensitivity analysis procedure based on the Monte Carlo Filtering (MCF) method that can be used to identify important parameters in the Mechanistic-Empirical (M-E) pavement design.