Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25497
Title: Damage detection utilising the artificial neural network methods to a benchmark structure
Authors: Wang, BS
Ni, YQ 
Ko, JM 
Keywords: ANN
Artificial neural network
Benchmark problem
Building structure
Damage detection
Structural health monitoring
Issue Date: 2011
Publisher: InterScience
Source: International journal of structural engineering, 2011, v. 2, no. 3, p. 229-242 How to cite?
Journal: International journal of structural engineering 
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.
URI: http://hdl.handle.net/10397/25497
ISSN: 1758-7328
DOI: 10.1504/IJSTRUCTE.2011.040782
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