Structural damage localization using probabilistic neural network

B. Wang, Yiqing Ni, J. Ko

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Structural damage detection based on vibration measurement has been a very active research subject during the past two decades. In recent years, there have been increasing researches focusing on the application of artificial neural networks (ANNs) in structural damage identification. Most of them perform well with numerical examples under error-free conditions, but become worse when the experimental data are polluted with measurement noise. In the paper, the probabilistic neural networks (PNNs) are applied for structural damage localization. The possible damage locations are considered as the pattern categories in PNNs. The damage location(s) is identified by means of the classification capacity of PNN. Two numerical examples are given in the paper. A performance comparison between the PNN and BP neural network for structural damage localization is carried out. The results show that with the same training and testing and testing samples, the PNN gives rise to a more accurate prediction in damage localization than the BP neural network.
Original languageEnglish
Pages (from-to)60-64
Number of pages5
JournalZhendong Gongcheng Xuebao/Journal of Vibration Engineering
Volume14
Issue number1
Publication statusPublished - 1 Mar 2001
Externally publishedYes

Keywords

  • Measurement noise
  • Neural network
  • Probabilistic neural network
  • Structural damage localization
  • Vibration measurement

ASJC Scopus subject areas

  • Civil and Structural Engineering

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