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A machine learning-based multi-scale computational framework for granular materials

Shaoheng Guan Guan, Tongming QU, Yuntian Feng Orcid Logo, Gang Ma Orcid Logo, Wei Zhou

Acta Geotechnica, Volume: 18, Issue: 4, Pages: 1699 - 1720

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

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Abstract

With the development of experimental measurement technology and high-fidelity numerical simulations of granular materials, empirical-based classical constitutive models may not be able to take full advantage of the rapidly increasing available datasets. Machine learning-based models can inherently a...

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Published in: Acta Geotechnica
ISSN: 1861-1125 1861-1133
Published: Springer Science and Business Media LLC 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60725
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Abstract: With the development of experimental measurement technology and high-fidelity numerical simulations of granular materials, empirical-based classical constitutive models may not be able to take full advantage of the rapidly increasing available datasets. Machine learning-based models can inherently avoid phenomenological assumptions to directly learn the constitutive relationship from the datasets, and the trained model is sufficiently flexible to be reconstructed once new training samples are added. In this work, a coupled finite element method and machine learning (FEM-ML) computational framework is proposed for simulating granular materials. Gaussian process-based random loading paths and coupled FEM-DEM simulations are used to generate training samples. A parametrisation of the material deformation history is used to represent the historical influence of granular materials. An uncertainty-level-based active learning is utilised to evaluate the informativeness of data points for network training and then to establish an effective resampling scheme from a massive dataset. Two examples are provided to show the applicability of the implemented FEM-ML framework. The performance of the proposed framework is also evaluated, the error is systematically analysed, and possible improvements are discussed. The results demonstrate that the FEM-ML framework offers considerable improvements in terms of computational efficiency and competence to simulate the mechanical responses of granular materials.
Keywords: DEM; FEM; Granular materials; Machine learning; Multi-scale
College: Faculty of Science and Engineering
Issue: 4
Start Page: 1699
End Page: 1720