Backcalculation of Non-Linear Pavement Moduli Using Finite-Element Based Neuro-Genetic Hybrid Optimization

Abstract

The determination of pavement layer stiffness is an essential step in evaluating the performance of existing road pavements and in conducting pavement design and analysis using mechanistic approaches. Over the years, several methodologies involving static, dynamic, and adaptive processes have been developed and proposed for obtaining in-situ pavement layer moduli from Falling Weight Deflectometer (FWD) test deflection data through inverse analysis and parameter identification routines. In this paper, a novel pavement analysis toolbox combining the strengths of Finite Element (FE) modeling, Neural Networks (NNs), and Genetic Algorithms (GAs) is described. The developed user-friendly automated pavement evaluation toolbox, referred to as Neuro-Genetic Optimization Toolbox (NGOT) can be used on a real-time basis for accurate and rapid transportation infrastructure evaluation. It is shown that the developed toolbox backcalculates non-linear pavement layer moduli from actual field data with better accuracy compared to regression and conventional backcalculation approaches.