Methodology for Global Sensitivity Analysis of Flexible Pavements in a Bayesian Back-Analysis Framework

摘要: AbstractProbabilistic sensitivity analysis is a crucial tool in the uncertainty of systems, which allows understanding how output response can be apportioned to different sources input parameters. Sobol’s method widely accepted global (GSA) technique that has been applied rank design parameters, based on their respective impact randomness. Although this variance-based highly efficient when parameters are independent, estimation Sobol indices presence correlation not sufficiently documented. This paper addresses shortcoming through development generalized for GSA Bayesian back-analysis framework, Kullback-Leibler (K-L) entropy measure serves as sensitivity. The methodology explored context flexible pavements mechanistic-empirical (M-E) whi...