Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

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外文摘要：One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architecture-enhanced MLP model,is crafted to replicate the history-dependent constitutive features of representa-tive volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

外文关键词：

Granular materialsHistory-dependenceMulti-layer perceptron(MLP)Discrete element methodFEM-DEMMachine learning

作者：

Mengqi Wang、Y.T.Feng、Shaoheng Guan、Tongming Qu

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作者单位：

Zienkiewicz Centre for Computational Engineering,Faculty of Science and Engineering,Swansea University,Swansea,Wales,SA1 8EP,UK

Institute of Theoretical and Computational Physics,Graz University of Technology,Graz,8010,Austria

Department of Civil and Environmental Engineering,Hong Kong University of Science and Technology,Clearwater Bay,Kowloon,Hong Kong,China

基金：

国家自然科学基金

项目编号：

12072217

出版年：

2024

DOI：

10.1016/j.jrmge.2024.02.003

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(6)