Subjects: Mechanics >> Applied Mechanics submitted time 2023-03-20 Cooperative journals: 《应用力学学报》
Abstract： Crack identification is an important issue in structural health monitoring.Based on the principleof inverse analysis , this paper combines the numerical manifold method (NMM) with the Elman neuralnetwork to carry out crack identification. To serve the learming of Elman neural network , the NMM is usedto obtain the displacement data of measuring points under various crack configurations.On this basis , thetrained Elman network is used for straight crack inversion. 'The feasibility and accuracy of NMM-Elmancollaborative method are verified by two typical examples.At the same time , the effects of measuring pointlayout and input data noise on crack inversion accuracy are analyzed.The research shows that the methodproposed in this paper can accurately reflect the crack tip coordinates of single and complex cracks.Thiswork provides a new pathway for efficient and accurate detection of complex cracks.
Subjects: Mechanics >> Applied Mechanics submitted time 2022-12-21 Cooperative journals: 《应用力学学报》
Abstract：
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.
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