General Geometric Reconstruction Method of Failure Criteria for Lattice Spring Model

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.

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Publication Details

Journal
International Journal for Numerical and Analytical Methods in Geomechanics
Published
2025-02-12
DOI
https://doi.org/10.1002/nag.3960
Primary Topic
Structural Response to Dynamic Loads
Type
article
Field-Weighted Citation Impact
0.00

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article

General Geometric Reconstruction Method of Failure Criteria for Lattice Spring Model

Gao‐Feng Zhao, Zhe Li, Zhen‐Qi Yang, Xin‐Dong Wei
International Journal for Numerical and Analytical Methods in Geomechanics
Structural Response to Dynamic Loads
article

General Geometric Reconstruction Method of Failure Criteria for Lattice Spring Model

Gao‐Feng Zhao, Zhe Li, Zhen‐Qi Yang, Xin‐Dong Wei
article en

Abstract

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.

International Journal for Numerical and Analytical Methods in GeomechanicsVol. 49(7)
Tianjin University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 97%
Structural Response to Dynamic Loads
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General Geometric Reconstruction Method of Failure Criteria for Lattice Spring Model — Gao‐Feng Zhao, Zhe Li, et al. · International Journal for Numerical and Analytical Methods in Geomechanics (2025) | TGRS Research Map | TGRS