Indian Journal of Engineering & Materials Sciences Vol. 12, February 2005, pp. 42-50 Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness Mehmet Saltan a & Serdal Terzi b a Civil Engineering Department, Engineering and Architectural Faculty, b Structural Education Department, Technical Education Faculty, Suleyman Demirel University, 32260 Isparta, Turkey Received 21 August 2003; accepted 27 October 2004 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. IPC Code: E01C 9/10 Highway pavements are generally constructed in the form of flexible pavements. Flexible pavements are layered systems with better materials on top and inferior materials at the bottom. Starting from the top, the pavement consists of wearing course, base and sub-base layers. The base material may be a bituminous mix or a granular material, depending on the number of heavy vehicles on the considered section of the road. However, local and cheaper materials can be used as a sub-base layer on top of the subgrade. Repeated application of vehicle loads, weather conditions and other factors decrease the serviceability of the pavement. For this reason, a maintenance program should be set up to decide when and where to carry out maintenance works. The most difficult aspect is to determine the remaining life of the pavement. In order to determine the remaining life, the pavement should be analyzed structurally with material properties for each layer being elastic modulus, Poisson’s ratio and thickness of layer. Non-destructive testing (NDT) and backcalculating pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. 1 NDT enables the use of a mechanistic approach for pavement design and rehabilitation because in-situ material properties may be backcalculated from the measured field data through appropriate analysis techniques 2 . In order to backcalculate reliable moduli, it is essential to accomplish several deflection tests at different locations along a highway section having relating uniform layer thicknesses 1 . But flexible pavement layer thicknesses must also be known to get realistic results. Layer thicknesses can be obtained by coring the flexible pavement. But it is important that non- destructive tests are carried out on flexible pavements for preventing to be damaged. Among non-destructive deflection measurement methods, commercially available devices are the Dynaflect, Road Rater and Falling Weight Deflectometer (FWD). FWD is commonly used in many countries. In recent years, one of the most important and promising research fields has been “Heuristics from Nature”, an area utilizing some analogies with natural or social systems and using them to derive non- deterministic heuristic methods and to obtain very good results. Artificial neural networks (ANN) and genetic algorithms (GA) are among the heuristic methods.