Application of Monte Carlo filtering method in regional sensitivity analysis of AASHTOWare Pavement ME design

Journal of Traffic and Transportation Engineering (English ed. Online)

Zhong Wu¹  Xiaohui Sun¹  Xiaoming Yang² 

[1] Louisiana Transportation Research Center, Baton Rouge, LA 70808, USA;School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USA;

关键词: Pavement design;    MEPDG;    Sensitivity analysis;    Monte Carlo filtering;   

DOI  :  10.1016/j.jtte.2017.03.006

来源: DOAJ

【 摘 要 】

Since AASHTO released the Mechanistic-Empirical Pavement Design Guide (MEPDG) for public review in 2004, many highway research agencies have performed sensitivity analyses using the prototype MEPDG design software. The information provided by the sensitivity analysis is essential for design engineers to better understand the MEPDG design models and to identify important input parameters for pavement design. In literature, different studies have been carried out based on either local or global sensitivity analysis methods, and sensitivity indices have been proposed for ranking the importance of the input parameters. In this paper, a regional sensitivity analysis method, Monte Carlo filtering (MCF), is presented. The MCF method maintains many advantages of the global sensitivity analysis, while focusing on the regional sensitivity of the MEPDG model near the design criteria rather than the entire problem domain. It is shown that the information obtained from the MCF method is more helpful and accurate in guiding design engineers in pavement design practices. To demonstrate the proposed regional sensitivity method, a typical three-layer flexible pavement structure was analyzed at input level 3. A detailed procedure to generate Monte Carlo runs using the AASHTOWare Pavement ME Design software was provided. The results in the example show that the sensitivity ranking of the input parameters in this study reasonably matches with that in a previous study under a global sensitivity analysis. Based on the analysis results, the strengths, practical issues, and applications of the MCF method were further discussed.

【 授权许可】

Unknown