Research on Application of Backpropagation Neural Network in Damage Detection of the Refined Plate Model

被引:0
作者
Teng, Wenxiang [1 ,2 ,3 ]
Qian, Cheng [1 ,2 ]
Yan, Leilei [1 ,2 ,3 ]
Shen, Gang [1 ,2 ,3 ]
Liu, Pengyu [1 ,2 ]
He, Jipeng [1 ,2 ]
Wang, Cheng [1 ,2 ]
机构
[1] Anhui Univ Sci & Technol Huainan, Huainan 232001, Peoples R China
[2] Anhui Univ Sci & Technol, State Key Lab Min Response & Disaster Prevent & Co, Huainan 232000, Anhui, Peoples R China
[3] Anhui Univ Sci & Technol, Min Intelligent Technol & Equipment Prov & Minist, Huainan 232001, Peoples R China
基金
中国国家自然科学基金;
关键词
damage detection; backpropagation neutral network (BPNN); carrera unified formulation (CUF); higher-order finite element; ELEMENTS;
D O I
10.1134/S0025654424603392
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
AbstractArtificial intelligence has been widely used in engineering. In this paper, we propose to combine the backpropagation neural network (BPNN) with the refined plate model based on Carrera Unified Formula (CUF) to advance the development of damage detection. The prediction model is built by utilizing the error back propagation function of the neural network. In addition, MATLAB uses Taylor's interpolation algorithm and lower degrees of freedom yet achieves the same accuracy as ANSYS, and the improved plate model accurately reproduces the mechanical properties of the metal plate. A database is then built based on the mechanical model to detect the location of damaged elements and node displacements. The nodal displacements were used as inputs while the locations of damaged elements were used as training outputs for the neural network. The effectiveness of the proposed method was verified through various damage scenarios. The results show that the method can accurately predict individual damage locations based on node displacements alone. The neural network combined with the plate model achieved a detection accuracy of 91% with a regression coefficient of 0.95.
引用
收藏
页码:1672 / 1688
页数:17
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