应用数学和力学2024，Vol.45Issue(2)：155-166,12.DOI:10.21656/1000-0887.440248
基于人工神经网络的颗粒材料本构关系及边值问题研究
Study on Constitutive Relations and Boundary Value Problems of Granular Materials Based on Artificial Neural Networks
摘要
Abstract
Granular materials are widely used in engineering practice,where the numerical simulation of boundary value problems related to granular materials is of great significance.By the artificial neural network algorithm,the discrete element method based on discrete particle models and the finite element method based on continuous models were organically combined to solve the boundary value problems of granular materials.A new and complete model and the solution were formed,namely,the micro-macroscopic 2-scale model and its solution system for offline calculation of the meso model.Specifically,the principal stress,the principal strain,and the corresponding stress-strain matrix of a granular material were first obtained based on the discrete ele-ment method.Then an artificial neural network model was built in the main space to describe the constitutive relationship of the granular material by an artificial neural network algorithm.Finally,the artificial neural net-work model was imported into ABAQUS to solve the boundary value problem of the granular material with the user-defined material subroutine UMAT.Through the numerical tests of plate compression and slope stability,and the comparison with the solution results of the classical elastoplastic model,it is seen that the trained artifi-cial neural network model can effectively reflect the constitutive relationship of granular materials,and can be used in practice to solve boundary value problems.The results show the feasibility of the solution scheme.关键词
颗粒材料/人工神经网络/离散元法/有限元法/边值问题Key words
granular material/artificial neural network/discrete element method/finite element method/boundary value problem分类
力学引用本文复制引用
张广江,杨德泽,楚锡华..基于人工神经网络的颗粒材料本构关系及边值问题研究[J].应用数学和力学,2024,45(2):155-166,12.基金项目
国家自然科学基金(12172263) （12172263）
