Application of Genetic Algorithm in Asphalt Pavement Design

Abstract

The main purpose of this study is to demonstrate the applicability of the genetic algorithm (GA) to solve nonlinear optimization problems encountered in asphalt pavement design. The fundamentals of the GA are briefly discussed, and three case studies are presented. The first case study is an example showing the backcalculation of layer moduli with deflection data from a falling weight deflectometer and a layered-elastic program. The second case study demonstrates how to construct the master curve, either from a mix flexural frequency sweep test or from a binder rheometer test, and how to apply the master curve in pavement design. The last case shows how to apply the GA to characterize the binder discrete relaxation spectrum with a generalized Maxwell solid model. The results indicate that the GA is successful in resolving the nonlinear optimization problem. The GA presents some difficulty in terms of computing efficiency; however, several techniques have been developed to alleviate this problem.