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The Health Monitoring of Bridges using Artificial Neural Networks

Published online by Cambridge University Press:  05 May 2011

Pei-Ling Liu  and
Shyh-Jang Sun
Pei-Ling Liu*
Affiliation:
Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan 10617, R.O.C.
Shyh-Jang Sun*
Affiliation:
Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan 10617, R.O.C.
*
*Professor
**Assistant Scientist
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Abstract

This study develops a neural network system to monitor the safety of a bridge structure. A truck of constant mass is driven at constant speed through the target bridge. Then, the maximal and minimal values of the bridge elongations are processed by a monitoring system to evaluate the current condition of the bridge. The monitoring system is composed of parallel backpropagation neural networks. Each neural network monitors a part of the bridge. The neural networks are trained using simulation data. The numerical example shows that the monitoring system is effective in the damage detection of the bridge.

Keywords

BridgeMonitoringArtificial neural networksElongationBackpropagation
Type
Articles
Information
Journal of Mechanics , Volume 17 , Issue 3 , September 2001 , pp. 157 - 166
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2001

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