Teng Wenxiang, Qian Cheng, Yan Leilei, Shen Gang, Liu Pengyu, He Jipeng, Wang Cheng
{"title":"反向传播神经网络在精制板模型损伤检测中的应用研究","authors":"Teng Wenxiang, Qian Cheng, Yan Leilei, Shen Gang, Liu Pengyu, He Jipeng, Wang Cheng","doi":"10.1134/S0025654424603392","DOIUrl":null,"url":null,"abstract":"<p>Artificial 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.</p>","PeriodicalId":697,"journal":{"name":"Mechanics of Solids","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Application of Backpropagation Neural Network in Damage Detection of the Refined Plate Model\",\"authors\":\"Teng Wenxiang, Qian Cheng, Yan Leilei, Shen Gang, Liu Pengyu, He Jipeng, Wang Cheng\",\"doi\":\"10.1134/S0025654424603392\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Artificial 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.</p>\",\"PeriodicalId\":697,\"journal\":{\"name\":\"Mechanics of Solids\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanics of Solids\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S0025654424603392\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanics of Solids","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1134/S0025654424603392","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MECHANICS","Score":null,"Total":0}
Research on Application of Backpropagation Neural Network in Damage Detection of the Refined Plate Model
Artificial 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.
期刊介绍:
Mechanics of Solids publishes articles in the general areas of dynamics of particles and rigid bodies and the mechanics of deformable solids. The journal has a goal of being a comprehensive record of up-to-the-minute research results. The journal coverage is vibration of discrete and continuous systems; stability and optimization of mechanical systems; automatic control theory; dynamics of multiple body systems; elasticity, viscoelasticity and plasticity; mechanics of composite materials; theory of structures and structural stability; wave propagation and impact of solids; fracture mechanics; micromechanics of solids; mechanics of granular and geological materials; structure-fluid interaction; mechanical behavior of materials; gyroscopes and navigation systems; and nanomechanics. Most of the articles in the journal are theoretical and analytical. They present a blend of basic mechanics theory with analysis of contemporary technological problems.