Genetic algorithm optimization for cohesive zone modeling of viscoelastic asphalt mixture fracture based on SCB test

Abstract

Cohesive zone model (CZM) has gained considerable attention to investigate the fracture mechanism of asphalt mixture. The parameter of CZM was generally determined through manually adjustment to match the numerical simulation to the experimental measurement. However, the method is time consuming and precision uncontrolled. This study introduced a novel approach to determine the CZM parameters based on an optimization approach. The semicircular bending test was conducted at an intermediate temperature to obtain the fracture mechanism of asphalt mixture. The Kriging model was implemented into the genetic algorithm as a surrogate model to predict the CZM parameters of bilinear cohesive law. A pre-select operation was proposed to enhance the computational performance. The result showed that the simulation with CZM parameters obtained from the optimization method matched well with the experimental measurement, indicating that the method could precisely characterize the fracture mechanics of asphalt mixture. The pre-select operation could achieve a more efficient optimization using few initial samples. The proposed approach provides an efficient procedure to characterize the fracture behavior of asphalt mixture.