Improved Multi-layer Analysis of Pavement Response Using Neural Networks to Optimize Numerical Integration

Abstract

This paper presents a new accurate method to compute the mechanical response of pavement structures using an Artificial Neural Network (ANN) model coupled with Multi-Layer Elastic Analysis (MLEA). The ANN model is used to improve the numerical integration of the response function used in the MLEA method. It requires four inputs: total pavement thickness, the diameter of the contact area, radial distance, and depth of the response point; and it was trained on one million hypothetical pavement structures. The developed method has been validated by a comparative analysis against boundary conditions, finite element analysis, and available MLEA solutions using various hypothetical pavement structures. The results demonstrate that the developed solution gives excellent response in the vicinity of the pavement surface together with a significant improvement in computational efficiency.

Комп’ютерні Технології Числового Моделювання Будівельних Конструкцій З Використанням Штучного Інтелекту

“…Ідея штучного інтелекту виникла на початку 1940-х років. Зараз ШІ використовується в багатьох сферах людського життя [1][2][3][4][5].…”