QUT ePrints

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Bandara, Rupika P., Chan, Tommy H.T., & Thambiratnam, David P. (2011) Structural damage identification with noise polluted Frequency Response Functions (FRFs). In Law, Siu-Seong, Cheng, Li, Xia, Yong, & Su, Znongqing (Eds.) Proceedings of the 14th Asia-Pacific Vibration Conference, Hong Kong Polytechnic University, Hong Kong Polytechnic University, Hong Kong.

This is the latest version of this eprint.

View at publisher

Abstract

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure.

This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels.

The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Impact and interest:

Citation countsare 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.

Full-text downloads:

220 since deposited on 13 Feb 2012
84 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 48579
Item Type: Conference Paper
Keywords: FRFs, Damage detection, Bench marks , Principal component analysis (PCA)
ISBN: 9789623677318
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: 14 Feb 2012 09:21
Last Modified: 14 Feb 2012 21:25

Available Versions of this Item

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page