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Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

Tongming Qu, Shaocheng Di, Yuntian Feng Orcid Logo, Min Wang, Tingting Zhao, Tongming QU

International Journal of Plasticity, Volume: 144, Start page: 103046

Swansea University Authors: Yuntian Feng Orcid Logo, Tongming QU

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DOI (Published version): 10.1016/j.ijplas.2021.103046

Abstract

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by...

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Published in: International Journal of Plasticity
ISSN: 0749-6419
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57046
first_indexed 2021-06-08T08:16:26Z
last_indexed 2025-02-25T05:37:20Z
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spelling 2025-02-24T11:55:11.6471793 v2 57046 2021-06-08 Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning d66794f9c1357969a5badf654f960275 0000-0002-6396-8698 Yuntian Feng Yuntian Feng true false 1a8144ef1058bc1310206808a4d274c3 Tongming QU Tongming QU true false 2021-06-08 ACEM The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The features and applications of each model are explained. Furthermore, the key factors for constitutive training, potential applications and deficiencies of the current work are also discussed in detail. Journal Article International Journal of Plasticity 144 103046 Elsevier BV 0749-6419 Deep learning, Data-drive, nElastic-plastic constitutive model, Gated Recurrent Unit (GRU), Granular materials, Micromechanics, Discrete element modelling 1 9 2021 2021-09-01 10.1016/j.ijplas.2021.103046 http://dx.doi.org/10.1016/j.ijplas.2021.103046 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2025-02-24T11:55:11.6471793 2021-06-08T09:13:15.3920446 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Tongming Qu 1 Shaocheng Di 2 Yuntian Feng 0000-0002-6396-8698 3 Min Wang 4 Tingting Zhao 5 Tongming QU 6 57046__20072__cdfd47c71a3846b2af8a979637c99ff4.pdf 57046.pdf 2021-06-08T09:15:49.7342148 Output 1379825 application/pdf Accepted Manuscript true 2022-06-15T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
spellingShingle Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
Yuntian Feng
Tongming QU
title_short Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
title_full Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
title_fullStr Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
title_full_unstemmed Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
title_sort Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
author_id_str_mv d66794f9c1357969a5badf654f960275
1a8144ef1058bc1310206808a4d274c3
author_id_fullname_str_mv d66794f9c1357969a5badf654f960275_***_Yuntian Feng
1a8144ef1058bc1310206808a4d274c3_***_Tongming QU
author Yuntian Feng
Tongming QU
author2 Tongming Qu
Shaocheng Di
Yuntian Feng
Min Wang
Tingting Zhao
Tongming QU
format Journal article
container_title International Journal of Plasticity
container_volume 144
container_start_page 103046
publishDate 2021
institution Swansea University
issn 0749-6419
doi_str_mv 10.1016/j.ijplas.2021.103046
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
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
url http://dx.doi.org/10.1016/j.ijplas.2021.103046
document_store_str 1
active_str 0
description The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The features and applications of each model are explained. Furthermore, the key factors for constitutive training, potential applications and deficiencies of the current work are also discussed in detail.
published_date 2021-09-01T11:41:09Z
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score 11.059359

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