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

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

doi:10.1007/s42947-022-00255-x

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

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

Engineering Systems & Supply Chain Management

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	14
Journal	International Journal of Pavement Research and Technology
Early online date	2 Dec 2022
DOIs	https://doi.org/10.1007/s42947-022-00255-x
Publication status	E-pub ahead of print - 2 Dec 2022

Bibliographical note

© 2022. This article is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/, 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.

Keywords

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

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10.1007/s42947-022-00255-xLicence: CC BY 4.0

s42947-022-00255-x
© 2022. This article is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/, 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.
Final published version, 2.7 MBLicence: CC BY 4.0

Cite this

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title = "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.",

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AU - Abed, Ahmed

AU - Thom, Nick

AU - Campos-Guereta, Ivan

AU - Airey, Gordon

N1 - © 2022. This article is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/, 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.

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N2 - 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.

AB - 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.

KW - Pavement response

KW - Multi-layer elastic analysis

KW - Numerical integration

KW - Artificial neural network

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DO - 10.1007/s42947-022-00255-x

M3 - Article

SN - 1996-6814

JO - International Journal of Pavement Research and Technology

JF - International Journal of Pavement Research and Technology

ER -

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

Abstract

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Keywords

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