Structural health monitoring via measured ritz vectors utilizing artificial neural networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

121 Scopus Citations
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Original languageEnglish
Pages (from-to)232-241
Journal / PublicationComputer-Aided Civil and Infrastructure Engineering
Volume21
Issue number4
Online published3 Mar 2006
Publication statusPublished - May 2006

Abstract

A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage inducedchanges in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology. © 2006 Computer-Aided Civil and Infrastructure Engineering.