Nondestructive Pavement Evaluation Using Finite Element Analysis Based Soft Computing Models

Evaluating structural condition of existing, in-service pavements constitutes annually a major part of the maintenance and rehabilitation activities undertaken by State Highway Agencies (SHAs). Accurate estimation of pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is very important for evaluating pavement’s structural condition, its remaining life for maintenance and rehabilitation purposes, and for properly incorporating life cycle cost considerations into an up to date, improved Pavement Management System. For this purpose, pavement deflection basins gathered from the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used to evaluate pavement structural conditions. Development of an innovative methodology, called SOFTSYS, Soft Computing Based Pavement and Geomaterial System Analyzer, is proposed here as an original way of interpreting the results of FWD tests for full-depth and conventional flexible pavements with the purpose of determining pavement layer properties as well as the layer thicknesses from FWD data without the need for pavement coring. Since the layer thickness information plays a crucial role in FWD data back calculation and remaining pavement life estimation, the outstanding contribution of SOFTSYS will be in the reliable estimation of pavement layer thicknesses in addition to their stiffness properties. Using only FWD test results (i.e. deflections) as inputs, SOFTSYS will calculate all the necessary properties for pavement evaluation. This study focused first on the use of ANN pavement structural models developed with the results of the ILLI-PAVE finite element (FE) program to predict pavement deflections under FWD loading. Then an innovative soft computing application, referred to herein as SOFTSYS, was introduced for the hybrid use of Genetic Algorithms (GAs) and artificial neural networks (ANNs) to estimate pavement layer properties including the hot mix asphalt concrete (HMA) thickness from only the FWD test data collected on full-depth asphalt pavements built on both natural and lime modified subgrades. The performances of the developed surrogate ANN structural models (forward models) were well above satisfactory; i.e., these ANN models could be used in lieu of finite element analyses for the quick and accurate predictions of the surface deflections and the critical responses of all types of full-depth flexible pavements found/constructed in Illinois, Indiana and Ohio.