Mahmoud Malakouti Olounabadi

In pavement engineering, determining the structural properties of the pavement, including the elastic coefficients and layer thicknesses, is highly significant. These properties determine the performance of the pavement and have a direct impact on the life of the pavement. Using commercial software for numerical simulation engines to calculate pavement surface changes increases the cost of calculations due to the complexity of integrating it into the optimization engine. In these methods, there is a need for a pre-generated artificial database using the software, as well as the use of a neural network and an optimization algorithm. Therefore, to generate the analysis population, the software must be run with a set of different estimation modules to provide the necessary population for inverse analysis, which increases the need for computational costs. The main goal of the current research is to combine the quadrature differential numerical method as an accurate, efficient, and high-speed numerical method with the Gray Wolf Optimization (GWO) metaheuristic optimization algorithm in order to inversely calculate the redundant values of the elastic modulus of pavement layers without using an artificial neural network and reducing the computational time. The results of the analysis with five independent runs showed that this method is able to achieve the desired response with a small number of populations and iterations.