Damage detection in structures using modified back-propagation neural networks |
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Authors: | Zhu Hongping Sima Yuzhou and Tang Jiaxiang |
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Institution: | (1) School of Civil Engineering & Mechanics, Huazhong University of Science & Technology, 430074 Wuhan, China |
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Abstract: | A nonparametric structural damage detection methodology based on neural networks method is presented for health monitoring
of structure-unknown systems. In this approach appropriate neural networks are trained by use of the modal test data from
a ‘healthy’ structure. The trained networks which are subsequently fed with vibration measurements from the same structure
in different stages have the capability of recognizing the location and the content of structural damage and thereby can monitor
the health of the structure. A modified back-propagation neural network is proposed to solve the two practical problems encountered
by the traditional back-propagation method, i.e., slow learning progress and convergence to a false local minimum. Various
training algorithms, types of the input layer and numbers of the nodes in the input layer are considered. Numerical example
results from a 5-degree-of-freedom spring-mass structure and analyses on the experimental data of an actual 5-storey-steel-frame
demonstrate that neural-networks-based method is a robust procedure and a practical tool for the detection of structural damage,
and that the modified back-propagation algorithm could improve the computational efficiency as well as the accuracy of detection
Project supported by the National Natural Science Foundation of China (No. 59908003) and the Natural Science Foundation of
Hubei Province (No. 99J035). |
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Keywords: | neural network modified back-propagation damage detection modal test data health monitoring |
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