Finite element based adaptive neuro‐fuzzy inference technique for parameter identification of multi‐layered transportation structures

Abstract

During the service life of a pavement, it is often required to conduct Non-destructive tests (NDTs) to evaluate its structural condition and bearing capacity and to detect damage resulting from the repeated traffic and environmental loading. Among several currently used NDT methods, the Falling Weight Deflectometer (FWD) is the most commonly used pavement NDT method applied by many transportation agencies all over the world. Non-destructive testing of pavements using FWD is typically accompanied by the prediction of the Young’s modulus of each layer of the pavement structure through an inverse analysis of the acquired FWD deflection data. The predicted pavement layer modulus is both an indicator of the structural condition of the layer as well as a required input for conducting mechanistic-based pavement structural analysis and design. Numerous methodologies have been proposed for backcalculating the mechanical properties of pavement structures from NDT data. This paper discusses the development of an Adaptive-Network-based Fuzzy Inference System (ANFIS) combined with Finite Element Modeling (FEM) for the inverse analysis of the multi-layered flexible pavement structures subjected to dynamic loading.

First published online: 27 Oct 2010

Keyword : transportation structures, non‐destructive testing,, pavement, neural networks, fuzzy inference, finite element