{"title":"General Geometric Reconstruction Method of Failure Criteria for Lattice Spring Model","authors":"Zhen‐Qi Yang, Xin‐Dong Wei, Zhe Li, Gao‐Feng Zhao","doi":"10.1002/nag.3960","DOIUrl":null,"url":null,"abstract":"Failure criteria are crucial in numerical methods for effectively resolving material failures, especially in geomaterials such as rock and concrete. A wide array of criteria, designed for different rock types and complex stress conditions, has been developed. Although Artificial Neural Network (ANN)‐based failure criteria present a unified solution for incorporating these diverse criteria into numerical models, their opaque nature and heavy data demands frequently curtail their practical utility and computational efficiency. To address these issues, this paper introduces a geometric‐based method for reconstructing existing failure criteria. This novel approach not only simulates the functionality of ANNs but also offers greater interpretability, simpler visualization, and reduced data requirements. The effectiveness of this reconstruction method in representing established failure criteria is validated, and its utility in a four‐dimensional lattice spring model (4D‐LSM) for modeling true triaxial conditions is showcased through multiple numerical examples.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"63 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Numerical and Analytical Methods in Geomechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/nag.3960","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Failure criteria are crucial in numerical methods for effectively resolving material failures, especially in geomaterials such as rock and concrete. A wide array of criteria, designed for different rock types and complex stress conditions, has been developed. Although Artificial Neural Network (ANN)‐based failure criteria present a unified solution for incorporating these diverse criteria into numerical models, their opaque nature and heavy data demands frequently curtail their practical utility and computational efficiency. To address these issues, this paper introduces a geometric‐based method for reconstructing existing failure criteria. This novel approach not only simulates the functionality of ANNs but also offers greater interpretability, simpler visualization, and reduced data requirements. The effectiveness of this reconstruction method in representing established failure criteria is validated, and its utility in a four‐dimensional lattice spring model (4D‐LSM) for modeling true triaxial conditions is showcased through multiple numerical examples.
期刊介绍:
The journal welcomes manuscripts that substantially contribute to the understanding of the complex mechanical behaviour of geomaterials (soils, rocks, concrete, ice, snow, and powders), through innovative experimental techniques, and/or through the development of novel numerical or hybrid experimental/numerical modelling concepts in geomechanics. Topics of interest include instabilities and localization, interface and surface phenomena, fracture and failure, multi-physics and other time-dependent phenomena, micromechanics and multi-scale methods, and inverse analysis and stochastic methods. Papers related to energy and environmental issues are particularly welcome. The illustration of the proposed methods and techniques to engineering problems is encouraged. However, manuscripts dealing with applications of existing methods, or proposing incremental improvements to existing methods – in particular marginal extensions of existing analytical solutions or numerical methods – will not be considered for review.