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Frequency response function based structural damage detection using artificial neural networks

Bandara, Rupika P., Chan, Tommy H.T., & Thambiratnam, David P. (2011) Frequency response function based structural damage detection using artificial neural networks. In International Conference on Structural Engineering, Construction and Management, 15 - 17 December 2011, Earl's Regency Hotel, Kandy, Sri Lanka.

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    Abstract

    Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm.

    In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks.

    Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

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    ID Code: 46380
    Item Type: Conference Paper
    Keywords: Frequency Response Functions, Principal Component Analysis, Back Propagation neural network, , Damage Severity, Damage Detection
    Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
    Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
    Past > Schools > School of Urban Development
    Copyright Owner: Copyright 2011 Rupika P. Bandara, Tommy H.T. Chan, & David P. Thambiratnam
    Deposited On: 11 Oct 2011 09:28
    Last Modified: 02 Feb 2012 21:38

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