Volume 43 Issue 11
Nov.  2022
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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
Citation: 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

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

doi: 10.21656/1000-0887.420250
  • 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
  • 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.

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