Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness

摘要

Pavement deflection data are often used to evaluate a pavement's structural condition non-destructively. It is essential not only to evaluate the structural integrity of an existing pavement but also to have accurate information on pavement surface condition in order to establish a reasonable pavement rehabilitation design system. Pavement layers are characterized by their elastic moduli estimated from surface deflections through backcalculation. Backcalculating the pavement layer moduli is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, flexible pavement layer thicknesses together with in-situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In this study, artificial neural networks (ANN) and gene expression programming (GEP) are used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the ANN and GEP approaches in backcalculating the pavement layer thickness and compared each other. These approaches can be easily and realistically performed to solve the optimization problems which do not have a formulation or function about the solution.