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Damage detection utilising the artificial neural network methods to a benchmark structure

Damage detection utilising the artificial neural network methods to a benchmark structure This paper discusses the damage identification using artificial neural network (ANN) methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and probabilistic neural network (PNN) are employed for damage localisation and BP network for damage extent identification. Four damage patterns (patterns 1-4) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localisation. The damage extent identification using back-propagation neural network (BPN) is successful even in Cases 2 and 5 and 6 in which the modelling error is quite large. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Structural Engineering Inderscience Publishers

Damage detection utilising the artificial neural network methods to a benchmark structure

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1758-7328
eISSN
1758-7336
DOI
10.1504/IJStructE.2011.040782
Publisher site
See Article on Publisher Site

Abstract

This paper discusses the damage identification using artificial neural network (ANN) methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and probabilistic neural network (PNN) are employed for damage localisation and BP network for damage extent identification. Four damage patterns (patterns 1-4) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localisation. The damage extent identification using back-propagation neural network (BPN) is successful even in Cases 2 and 5 and 6 in which the modelling error is quite large.

Journal

International Journal of Structural Engineering – Inderscience Publishers

Published: Jan 1, 2011

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