Intelligent Crack Recognition Based on XFEM and GA-BP Neural Networks北大核心CSCD

基于扩展有限元法(XFEM)和经遗传算法(GA)优化的误差反向传播多层前馈(BP)神经网络(GA-BP)算法，建立了识别结构中裂纹的反演分析模型。模型通过XFEM正向分析获得的测点位移数据训练GA-BP神经网络，并在此基础上利用该网络进行裂纹反向识别。通过两个典型算例对模型的可行性和精度进行了验证，并探讨了网格密度、测点布置、输入数据噪声等对网络识别精度的影响。结果表明，该文的方法可反演线弹性断裂力学重点关注的直线裂纹的几何信息且具有较好的容噪性能，此外，GA-BP神经网络的预测精度较传统BP神经网络普遍更高。

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

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.