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.

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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.

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ID Code: 48579
Item Type: Conference Paper
Refereed: Yes
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: 13 Feb 2012 23:21
Last Modified: 14 Feb 2012 11:25

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