Performance of neural networks for simulation and prediction of temperature-induced modal variability

H. F. Zhou, Yiqing Ni, J. M. Ko

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Vibration-based damage detection methods use changes in modal parameters to diagnose structural degradation or damage. Structures in reality are subject to varying environmental effects which also cause changes in modal parameters. The well-defined nature of the environmental effects on modal properties is essential for reliable damage diagnosis based on vibration measurement. In this paper, the performance of artificial neural networks (ANNs) for simulation and prediction of temperature-caused variability of modal frequencies is investigated. Making use of one-year measurement data of modal frequencies and temperatures from an instrumented cable-stayed bridge, three- layer back-propagation (BP) neural networks are configured to model the correlation between the temperatures and frequencies. Two approaches are adopted in defining the training samples to train the neural networks and the testing samples to verify the prediction capability of the neural networks. It is shown that when using appropriate training data covering a wide range of temperature variations, the trained neural networks exhibit satisfactory performance in both reproduction (simulation) and prediction (generalization). A good mapping between the temperatures and frequencies is obtained by the neural network models for all measured modes.
Original languageEnglish
Article number102
Pages (from-to)912-922
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5765
Issue numberPART 2
DOIs
Publication statusPublished - 29 Sep 2005
EventSmart Structures and Materials 2005 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems - San Diego, CA, United States
Duration: 7 Mar 200510 Mar 2005

Keywords

  • Artificial neural networks (ANNs)
  • Cable-stayed bridges
  • Modal variability
  • Structural health monitoring
  • Temperature effect

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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