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Structural damage detection using frequency response functions and neural networks

Bandara, Rupika Priyadarshani, Chan, Tommy H.T., & Thambiratnam, David P. (2011) Structural damage detection using frequency response functions and neural networks. In Alcocer , Sergio M., Carrión, Francisco, Gómez-Martínez, Roberto, & Alexis , Méndez (Eds.) 5th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-5) 2011, 11-15 December 2011, The Ritz-Carlton, Cancún, México .

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    Abstract

    This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA).

    In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

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    ID Code: 48576
    Item Type: Conference Paper
    Keywords: Neural Networks, Frequency Response Functions, Principal Component Analysis
    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 Universidad Nacional Autónoma de México
    Copyright Statement: All rights reserved. Reproduction or translation of any part of this work without the permission of the copyright owner is unlawful.
    Deposited On: 14 Feb 2012 09:27
    Last Modified: 16 Feb 2012 15:22

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