Study on Structural Damage Detection Using RBF Network

Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The train...

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Bibliographic Details
Published inApplied Mechanics and Materials Vol. 578-579; no. Advances in Civil Structures IV; pp. 1125 - 1128
Main Authors Fan, Jin Sheng, Cao, Xiu Ling, Yuan, Ying
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 04.07.2014
Subjects
Damage detection
Finite element method
Frame structures
Mathematical analysis
Networks
Neural networks
Shape recognition
Structural damage
Mode Shape Curvature Ratio
RBF Neural Network
Damage Detection
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Summary:Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The trained neural network can identify the damage location and degree of the frame structure. It proved that this method is simple and valid.
Bibliography:Selected, peer reviewed papers from the 4th International Conference on Civil Engineering, Architecture and Building Materials (CEABM 2014), May 24-25, 2014, Haikou, China
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ISBN:3038351644
9783038351641
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.578-579.1125