Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

Askari, M; Li, J; Samali, B

Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

Askari, M Li, J

Samali, B

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Publication Type:: Journal Article
Citation:: International Journal of Structural Stability and Dynamics, 2016, 16 (6)
Issue Date:: 2016-08-01

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Field	Value	Language
dc.contributor.author	Askari, M	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.date.available	2020-05-25T19:21:29Z
dc.date.issued	2016-08-01	en_US
dc.identifier.citation	International Journal of Structural Stability and Dynamics, 2016, 16 (6)	en_US
dc.identifier.issn	0219-4554	en_US
dc.identifier.uri	http://hdl.handle.net/10453/95741
dc.description.abstract	© 2016 World Scientific Publishing Company. System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input-output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input-output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.	en_US
dc.relation.ispartof	International Journal of Structural Stability and Dynamics	en_US
dc.relation.isbasedon	10.1142/S0219455415500169	en_US
dc.title	Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	16	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0913 Mechanical 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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

© 2016 World Scientific Publishing Company. System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input-output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input-output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

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http://hdl.handle.net/10453/95741