A Synergistic XFEM-RBF-MOPSO Based Approach for Multi-Crack Parameter Inversion

Articles in Press

Document Type : Research Paper

Authors
Guizhong Xie 1
Zhengyang Zhou 1
Hao Li 1
Hongfei Zhai 1
Chao Wu 1
Shenshen Chen 2
Jun Liu 3
Yang Liu 3
Jie Zhou 3
1 Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China
2 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, 330013, Jiangxi, China
3 State Key Laboratory of Mining Heavy Equipment, CITIC Heavy Industries Co., Ltd, Luoyang, 471039, Henan, China
10.22055/jacm.2025.48636.5383
Abstract
This paper proposes an integrated XFEM-RBF-MOPSO framework for the inversion of multiple crack parameters from displacement data. High-fidelity forward response samples are generated by the extended finite element method (XFEM) using optimal Latin hypercube sampling (OLHS). The XFEM results are used to construct a radial basis function (RBF) surrogate, which approximates the mapping between crack parameters and displacement responses. Multi-objective particle swarm optimization (MOPSO) is employed to search the parameter space using the RBF surrogate in order to match the observed displacements, thereby reducing the computational cost. This is because the RBF surrogate has higher predictive accuracy than the Kriging surrogate under limited-sample conditions, as verified by cross-validation. Our approach is validated through three numerical examples, including collinear double-edge cracks, two parallel internal cracks, and three parallel internal cracks. The crack-tip coordinates obtained by the combined MOPSO-RBF approach are in good agreement with those of the actual cases. The results demonstrate better convergence and computational efficiency, indicating that the proposed approach is a promising approach for practical multi-crack inversion.
Keywords
Crack inversion
Extended finite element method
Radial basis function
Multi-objective optimization
Particle swarm algorithm
Subjects

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Available Online from 29 December 2025