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.
Highlights
Since pavement structures wear down and deteriorate under heavy axle loadings and environmental in uences, they need to be maintained and rehabilitated on a regular basis. is requires a very signi cant commitment of resources on the part of nation’s highway agencies at the State, Federal and local levels
E objective of this paper is to investigate the feasibility of using Adaptive-Network-based Fuzzy Inference System (ANFIS) for the inverse analysis of the multi-layered exible pavement structures based on Falling Weight De ectometer (FWD) data
A Finite Element (FE) model is employed to envisage the response of the pavement to FWD load with the known characteristics of pavement materials. e FE model captures the non-linear, stressdependent behavior of geo-materials used in the underlying unbound pavement layers resulting in realistic materials characterization and modeling responses
Summary
Since pavement structures wear down and deteriorate under heavy axle loadings and environmental in uences, they need to be maintained and rehabilitated on a regular basis. is requires a very signi cant commitment of resources on the part of nation’s highway agencies at the State, Federal and local levels. Inverse or back analysis is used to determine the Young’s modulus of pavement layers based on the measured de ection data In this process, more commonly referred to as backcalculation, a numerical optimization doi: 10.3846 / transport.2010.08. E objective of this paper is to investigate the feasibility of using Adaptive-Network-based Fuzzy Inference System (ANFIS) for the inverse analysis of the multi-layered exible pavement structures based on FWD data. In this approach, a Finite Element (FE) model is employed to envisage the response of the pavement to FWD load with the known characteristics of pavement materials. A Finite Element (FE) model is employed to envisage the response of the pavement to FWD load with the known characteristics of pavement materials. e FE model captures the non-linear, stressdependent behavior of geo-materials used in the underlying unbound pavement layers resulting in realistic materials characterization and modeling responses
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