Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

Abstract

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.