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

B. S. Wang, Yiqing Ni, J. M. Ko

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)229-242
Number of pages14
JournalInternational Journal of Structural Engineering
Issue number3
Publication statusPublished - 30 Nov 2011


  • ANN
  • Artificial neural network
  • Benchmark problem
  • Building structure
  • Damage detection
  • Structural health monitoring

ASJC Scopus subject areas

  • Civil and Structural Engineering


Dive into the research topics of 'Damage detection utilising the artificial neural network methods to a benchmark structure'. Together they form a unique fingerprint.

Cite this