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Design of artificial neural networks for structural health monitoring

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Abstract

    This paper addresses the problem of structural health monitoring (damage detection) based on the pattern matching approach utilizing dynamic data. Artificial Neural Networks (ANNs) are employed as tools for matching the "damage patterns" for the purpose of detecting the damage location and the corresponding damage extent. This paper concentrates on the design of ANNs, which is usually neglected or only slightly addressed in the literature. It is very clear that the selection of the class of feedforward ANN models, that is to decide the number of hidden layers and the number of nodes in each hidden layer, has significant effect on both the training of ANNs and the performance of the trained ANNs. In this paper, an ANN class selection method, which follows the Bayesian probabilistic approach, is proposed. A five-story building example is used to demonstrate the proposed methodology. © 2003 Swets & Zeitlinger, Lisse.
    Original languageEnglish
    Title of host publicationStructural Health Monitoring and Intelligent Infrastructure
    Subtitle of host publicationProceedings of the first International Conference on Structural Health Monitoring and Intelligent Infrastructure
    EditorsZ.S. Wu, M. Abe
    PublisherSwets & Zeitlinger
    Pages611-618
    Volume1
    ISBN (Print)9789058096487, 9058096475 (2-v. set), 9058096483 (v. 1), 9058096491 (v. 2)
    Publication statusPublished - 2003
    Event1st International Conference on Structural Health Monitoring and Intelligent Infrastructure (SHMII-1) - Tokyo, Japan
    Duration: 13 Nov 200315 Nov 2003

    Publication series

    Name
    Volume1

    Conference

    Conference1st International Conference on Structural Health Monitoring and Intelligent Infrastructure (SHMII-1)
    PlaceJapan
    CityTokyo
    Period13/11/0315/11/03

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