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Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning
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Min Wang,
Tingting Zhao,
Tongming QU
International Journal of Plasticity, Volume: 144, Start page: 103046
Swansea University Authors:
Yuntian Feng , 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...
| Published in: | International Journal of Plasticity |
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| ISSN: | 0749-6419 |
| Published: |
Elsevier BV
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57046 |
| 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 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. |
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| Keywords: |
Deep learning, Data-drive, nElastic-plastic constitutive model, Gated Recurrent Unit (GRU), Granular materials, Micromechanics, Discrete element modelling |
| College: |
Faculty of Science and Engineering |
| Start Page: |
103046 |