Intelligent Crack Recognition Based on XFEM and GA-BP Neural Networks

MAO Xiaomin; ZHANG Huihua; JI Xiaolei; HAN Shangyu

doi:10.21656/1000-0887.420250

Volume 43 Issue 11

Nov. 2022

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Article Navigation > Applied Mathematics and Mechanics > 2022 > 43(11): 1268-1280

MAO Xiaomin, ZHANG Huihua, JI Xiaolei, HAN Shangyu. Intelligent Crack Recognition Based on XFEM and GA-BP Neural Networks[J]. Applied Mathematics and Mechanics, 2022, 43(11): 1268-1280. doi: 10.21656/1000-0887.420250

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Intelligent Crack Recognition Based on XFEM and GA-BP Neural Networks

doi: 10.21656/1000-0887.420250

School of Civil Engineering and Architecture, Nanchang Hangkong University, Nanchang 330063, P.R.China

Received Date: 2021-08-25
Accepted Date: 2022-02-01
Rev Recd Date: 2021-12-20

Available Online: 2022-10-15

Publish Date: 2022-11-30

Abstract

Abstract

Based on the extended finite element method (XFEM) and the error back propagation (BP) multilayer feedforward neural network algorithm optimized by the genetic algorithm (GA), an inverse analysis model for identifying cracks in structures was established. The GA-BP neural network was trained by the displacement data of measuring points obtained by the XFEM forward analysis, and the network was used for crack inverse identification. The feasibility and accuracy of the model were verified with 2 typical examples, and the effects of the mesh density, the measuring point layout and the input data noise on the accuracy of network recognition were discussed. The results show that, the proposed method can invert the geometric information of straight cracks, which is the major focus of linear elastic fracture mechanics, and has good noise tolerance. Besides, the GA-BP neural network has higher accuracy than the traditional BP neural network in general.
- extended finite element method,
- genetic algorithm,
- BP neural network,
- inverse analysis,
- crack

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References(23)

References

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