Citation

Oveysifar M, Abedini M. Road 2023; 31(117): 195-212.

Copyright

(Copyright © 2023, Road, Housing and Urban Development Research Center)

DOI

10.22034/road.2023.382270.2134

PMID

unavailable

Abstract

The speed of vehicles can affect the performance and damage of the pavement, The two major failures in flexible pavements are fatigue cracks and rutting. The tensile stress under overlay and the compressive stress on the subgrade depend on various factors such as the speed and amount of passing load and contact surface pressure. In this research, machine learning algorithms have been used to predict the critical failure of flexible pavement based on vehicle speed. In the process of using neural networks, the data of neural networks first selects a series of random values as the primary weights and biases of the network which is one of its deficiencies. Therefore, Particle Swarm Optimization (PSO) algorithm is used to optimize the weight of neural networks. In comparison with optimization algorithms, PSO is simpler for implementation and can rapidly find optimal point. According to the modeling results, at low vehicle speeds, the probability of fatigue failures, rutting, and settling increases, while the lower the speed of the vehicle, the number of repetitions is reduced, and the increase in speed causes that Less damage to the pavement.

Language: en