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 .

[img] Published Version (PDF 238kB)
Available to QUT staff and students only | Request a copy from author

View at publisher


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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 48576
Item Type: Conference Paper
Refereed: Yes
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: 13 Feb 2012 23:27
Last Modified: 16 Feb 2012 05:22

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page