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Data-driven strain–stress modelling of granular materials via temporal convolution neural network

Mengqi Wang, Tongming QU, Shaoheng Guan Guan, Tingting Zhao, Biao Liu, Yuntian Feng Orcid Logo

Computers and Geotechnics, Volume: 152, Start page: 105049

Swansea University Authors: Mengqi Wang, Tongming QU, Shaoheng Guan Guan, Yuntian Feng Orcid Logo

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DOI (Published version): 10.1016/j.compgeo.2022.105049

Abstract

Machine learning offers a new approach to predicting the path-dependent stress–strain response of granular materials. Recent studies show that temporal convolution neural (TCN) networks, a mutation of the 1D convolution neural network (CNN), have a powerful capability of addressing time-related pred...

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Published in: Computers and Geotechnics
ISSN: 0266-352X
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61307
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Recent studies show that temporal convolution neural (TCN) networks, a mutation of the 1D convolution neural network (CNN), have a powerful capability of addressing time-related prediction tasks. In this work, TCN networks are constructed to explore their potential in capturing the constitutive law of granular materials. To train and test the TCN network, three types of numerical experiments are implemented to generate datasets via discrete element modelling. The Bayesian optimisation method is employed to find the optimum architecture of the network. Furthermore, to improve the training accuracy and efficiency, a transfer learning (TL) scheme is innovatively leveraged, which utilises the trained network parameters from a set of shorter time steps and/or coarser data points of the training strain&#x2013;stress loading curves, as the initial values, to train the network for a longer time step. The prediction ability of the trained TCN network is assessed and compared with a recurrent neural network which has been proved to perform well in predicting constitutive laws of the granular materials. 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spelling 2022-12-19T15:05:25.9778986 v2 61307 2022-09-23 Data-driven strain–stress modelling of granular materials via temporal convolution neural network 9ac548c60cf55e904db6918afe302681 Mengqi Wang Mengqi Wang true false 1a8144ef1058bc1310206808a4d274c3 Tongming QU Tongming QU true false 8be5dace79e94a4d0abd32215a13f806 Shaoheng Guan Guan Shaoheng Guan Guan true false d66794f9c1357969a5badf654f960275 0000-0002-6396-8698 Yuntian Feng Yuntian Feng true false 2022-09-23 FGSEN Machine learning offers a new approach to predicting the path-dependent stress–strain response of granular materials. Recent studies show that temporal convolution neural (TCN) networks, a mutation of the 1D convolution neural network (CNN), have a powerful capability of addressing time-related prediction tasks. In this work, TCN networks are constructed to explore their potential in capturing the constitutive law of granular materials. To train and test the TCN network, three types of numerical experiments are implemented to generate datasets via discrete element modelling. The Bayesian optimisation method is employed to find the optimum architecture of the network. Furthermore, to improve the training accuracy and efficiency, a transfer learning (TL) scheme is innovatively leveraged, which utilises the trained network parameters from a set of shorter time steps and/or coarser data points of the training strain–stress loading curves, as the initial values, to train the network for a longer time step. The prediction ability of the trained TCN network is assessed and compared with a recurrent neural network which has been proved to perform well in predicting constitutive laws of the granular materials. In addition, training datasets with artificially added noise are also used to test and analyse the robustness of TCN networks. Journal Article Computers and Geotechnics 152 105049 Elsevier BV 0266-352X Machine learning; Stress–strain relations; Temporal convolution neural (TCN) networks; Bayesian optimisation; Transfer learning; Discrete element modelling 1 12 2022 2022-12-01 10.1016/j.compgeo.2022.105049 Data will be made available on request. COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) National Natural Science Foundation of China (NSFC) (Grant Nos. 12102294 and 12072217). 2022-12-19T15:05:25.9778986 2022-09-23T10:13:36.0176169 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Mengqi Wang 1 Tongming QU 2 Shaoheng Guan Guan 3 Tingting Zhao 4 Biao Liu 5 Yuntian Feng 0000-0002-6396-8698 6 61307__25837__03fdb71e02cc406db5599b77b718059a.pdf 61307.pdf 2022-11-20T12:02:44.3310582 Output 16465685 application/pdf Version of Record true © 2022 The Author(s). This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Data-driven strain–stress modelling of granular materials via temporal convolution neural network
spellingShingle Data-driven strain–stress modelling of granular materials via temporal convolution neural network
Mengqi Wang
Tongming QU
Shaoheng Guan Guan
Yuntian Feng
title_short Data-driven strain–stress modelling of granular materials via temporal convolution neural network
title_full Data-driven strain–stress modelling of granular materials via temporal convolution neural network
title_fullStr Data-driven strain–stress modelling of granular materials via temporal convolution neural network
title_full_unstemmed Data-driven strain–stress modelling of granular materials via temporal convolution neural network
title_sort Data-driven strain–stress modelling of granular materials via temporal convolution neural network
author_id_str_mv 9ac548c60cf55e904db6918afe302681
1a8144ef1058bc1310206808a4d274c3
8be5dace79e94a4d0abd32215a13f806
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author_id_fullname_str_mv 9ac548c60cf55e904db6918afe302681_***_Mengqi Wang
1a8144ef1058bc1310206808a4d274c3_***_Tongming QU
8be5dace79e94a4d0abd32215a13f806_***_Shaoheng Guan Guan
d66794f9c1357969a5badf654f960275_***_Yuntian Feng
author Mengqi Wang
Tongming QU
Shaoheng Guan Guan
Yuntian Feng
author2 Mengqi Wang
Tongming QU
Shaoheng Guan Guan
Tingting Zhao
Biao Liu
Yuntian Feng
format Journal article
container_title Computers and Geotechnics
container_volume 152
container_start_page 105049
publishDate 2022
institution Swansea University
issn 0266-352X
doi_str_mv 10.1016/j.compgeo.2022.105049
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description Machine learning offers a new approach to predicting the path-dependent stress–strain response of granular materials. Recent studies show that temporal convolution neural (TCN) networks, a mutation of the 1D convolution neural network (CNN), have a powerful capability of addressing time-related prediction tasks. In this work, TCN networks are constructed to explore their potential in capturing the constitutive law of granular materials. To train and test the TCN network, three types of numerical experiments are implemented to generate datasets via discrete element modelling. The Bayesian optimisation method is employed to find the optimum architecture of the network. Furthermore, to improve the training accuracy and efficiency, a transfer learning (TL) scheme is innovatively leveraged, which utilises the trained network parameters from a set of shorter time steps and/or coarser data points of the training strain–stress loading curves, as the initial values, to train the network for a longer time step. The prediction ability of the trained TCN network is assessed and compared with a recurrent neural network which has been proved to perform well in predicting constitutive laws of the granular materials. In addition, training datasets with artificially added noise are also used to test and analyse the robustness of TCN networks.
published_date 2022-12-01T04:20:03Z
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score 11.013148

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