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

Ahmed Abed, Nick Thom, Ivan Campos-Guereta, Gordon Airey

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Number of pages14
JournalInternational Journal of Pavement Research and Technology
Early online date2 Dec 2022
Publication statusE-pub ahead of print - 2 Dec 2022

Bibliographical note

© 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.


  • Pavement response
  • Multi-layer elastic analysis
  • Numerical integration
  • Artificial neural network


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