Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures

Makki Alamdari, M; Anaissi, A; Khoa, NLD; Mustapha, S

Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures

Makki Alamdari, M Anaissi, A Khoa, NLD Mustapha, S

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Publication Type:: Journal Article
Citation:: Structural Control and Health Monitoring, 2019, 26 (2)
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Makki Alamdari, M	en_US
dc.contributor.author	Anaissi, A	en_US
dc.contributor.author	Khoa, NLD	en_US
dc.contributor.author	Mustapha, S	en_US
dc.date.accessioned	2020-04-16T23:37:57Z
dc.date.available	2020-04-16T23:37:57Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	Structural Control and Health Monitoring, 2019, 26 (2)	en_US
dc.identifier.issn	1545-2255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/140057
dc.description.abstract	© 2018 John Wiley & Sons, Ltd. In this paper, we focused on the development and verification of a solid and robust framework for structural condition assessment of real-life structures using measured vibration responses, with the presence of multiple progressive damages occurring within the inspected structures. A self-tuning learning method for structural condition assessment was proposed. Damage sensitive features were extracted using a frequency domain decomposition (FDD) approach to fuse all the measured responses, followed by random projection algorithm for dimensionality reduction. An automatic parameter selection method called Appropriate Distance to the Enclosing Surface (ADES) was used for tuning the classifier parameter. The effect of operational conditions on the robustness of the proposed method was also investigated, and it was realized that application of FDD to extract damage sensitive feature reduces the variation in the results. Promising results in the assessment of damage were obtained based on two comprehensive case studies, which included single and multiple damage scenarios. The contributions of the work are threefold. First, through two comprehensive case studies, we demonstrate that the frequency-based feature from a single sensor might not be adequate enough to detect the progress of damage, even if the sensor is in the vicinity of damage. Second, we show that data fusion using FDD can reliably assess the severity of damage, and finally, we propose a new automated approach for tuning the classifier parameter.	en_US
dc.relation.ispartof	Structural Control and Health Monitoring	en_US
dc.relation.isbasedon	10.1002/stc.2299	en_US
dc.subject.classification	Civil Engineering	en_US
dc.title	Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
utslib.for	0905 Civil Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2018 John Wiley & Sons, Ltd. In this paper, we focused on the development and verification of a solid and robust framework for structural condition assessment of real-life structures using measured vibration responses, with the presence of multiple progressive damages occurring within the inspected structures. A self-tuning learning method for structural condition assessment was proposed. Damage sensitive features were extracted using a frequency domain decomposition (FDD) approach to fuse all the measured responses, followed by random projection algorithm for dimensionality reduction. An automatic parameter selection method called Appropriate Distance to the Enclosing Surface (ADES) was used for tuning the classifier parameter. The effect of operational conditions on the robustness of the proposed method was also investigated, and it was realized that application of FDD to extract damage sensitive feature reduces the variation in the results. Promising results in the assessment of damage were obtained based on two comprehensive case studies, which included single and multiple damage scenarios. The contributions of the work are threefold. First, through two comprehensive case studies, we demonstrate that the frequency-based feature from a single sensor might not be adequate enough to detect the progress of damage, even if the sensor is in the vicinity of damage. Second, we show that data fusion using FDD can reliably assess the severity of damage, and finally, we propose a new automated approach for tuning the classifier parameter.

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http://hdl.handle.net/10453/140057