Automated Operational Modal Analysis of a Cable-Stayed Bridge

Sun, M; Makki Alamdari, M; Kalhori, H

Automated Operational Modal Analysis of a Cable-Stayed Bridge

Sun, M Makki Alamdari, M Kalhori, H

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Publication Type:: Journal Article
Citation:: Journal of Bridge Engineering, 2017, 22 (12)
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Sun, M	en_US
dc.contributor.author	Makki Alamdari, M	en_US
dc.contributor.author	Kalhori, H https://orcid.org/0000-0002-1908-4895	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Journal of Bridge Engineering, 2017, 22 (12)	en_US
dc.identifier.issn	1084-0702	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123622
dc.description.abstract	© 2017 American Society of Civil Engineers. Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented.	en_US
dc.relation.ispartof	Journal of Bridge Engineering	en_US
dc.relation.isbasedon	10.1061/(ASCE)BE.1943-5592.0001141	en_US
dc.subject.classification	Civil Engineering	en_US
dc.title	Automated Operational Modal Analysis of a Cable-Stayed Bridge	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	22	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

© 2017 American Society of Civil Engineers. Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented.

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http://hdl.handle.net/10453/123622