基于人工神经网络的颗粒材料本构关系及边值问题研究OA北大核心CSTPCD

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.