Structural damage detection method using frequency response functions

Bandara, Rupika P., Chan, Tommy H.T., & Thambiratnam, David P. (2014) Structural damage detection method using frequency response functions. Structural Health Monitoring, 13(4), pp. 418-429.

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Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

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3 citations in Scopus
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3 citations in Web of Science®

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ID Code: 80168
Item Type: Journal Article
Refereed: Yes
Keywords: Structural damage detection, pattern recognition, artificial neural networks, principal component analysis, frequency response functions, damage quantification
DOI: 10.1177/1475921714522847
ISSN: 1741-3168
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Author(s)
Deposited On: 21 Jan 2015 00:49
Last Modified: 21 Jan 2015 21:09

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