A novel information-gap technique to assess reliability of neural network-based damage detection

S.G. Pierce, K. Worden, G. Manson

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34 Citations (Scopus)


The application of neural network classifiers to a damage detection problem is discussed within a framework of an interval arithmetic-based information-gap technique. Using this approach the robustness of trained classifiers to uncertainty in their input data was assessed. Conventional network training using a regularised Maximum Likelihood approach is discussed and compared with interval propagation applied as a tool to evaluate the robustness of a particular network. Concepts of interval-based worst-case error and opportunity are introduced to facilitate the analysis. The interval-based approach is further developed into a network selection procedure capable of significant improvements (up to 22%) in the worst-case error performance over a conventional network trained on crisp (single-valued) data.
Original languageEnglish
Pages (from-to)96-111
Number of pages15
JournalJournal of Sound and Vibration
Issue number1-2
Publication statusPublished - 30 May 2006


  • uncertain system
  • defect detection
  • interval arithmetic
  • neural networks

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