基于XFEM与BP神经网络的裂纹智能识别

Intelligent crack identification based on XFEM and BP neural network

数值技术与智能算法的飞速发展为结构内部缺陷的识别提供了新途径。本研究建立了扩展有限元法(XFEM)与误差反向传播多层前馈(BP)神经网络相结合的裂纹反演分析模型。模型通过XFEM正向分析获得的测点位移数据训练BP神经网络,在此基础上利用该网络进行裂纹反向识别。通过2个典型算例验证了模型的可行性和精度,结果表明本研究的方法能够准确反演裂纹的几何信息,与此同时还探讨了测点布置方式及输入数据噪声等因素对识别精度的影响。

The rapid development of numerical technology and intelligent algorithm provides a new way to identify the internal defects of structures.In this paper,an inverse analysis model for crack detection is established by combining extended finite element method(XFEM)and error-back-propagation multilayer feedforward(BP)neural network.The BP neural network is trained by the displacement data obtained from the forward analysis of XFEM.On this basis,the network is used for the inverse identification of cracks.The feasibility and accuracy of the model are verified by two typical examples.The results show that the proposed method can accurately retrieve the geometric information of cracks.At the same time,the influence of the layout of measuring points and the input data noise on the identification accuracy is also discussed.