Real-Time Tracking of Structural Stiffness Reduction with Unknown Inputs, Using Self-Adaptive Recursive Least-Square and Curvature-Change Techniques

Askari, M; Yu, Y; Zhang, C; Samali, B; Gu, X

Real-Time Tracking of Structural Stiffness Reduction with Unknown Inputs, Using Self-Adaptive Recursive Least-Square and Curvature-Change Techniques

Askari, M Yu, Y

Zhang, C Samali, B Gu, X

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Publisher:: World Scientific Pub Co Pte Lt
Publication Type:: Journal Article
Citation:: International Journal of Structural Stability and Dynamics, 2019, 19, (10)
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Askari, M
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Zhang, C
dc.contributor.author	Samali, B
dc.contributor.author	Gu, X https://orcid.org/0000-0003-3161-8391
dc.date.accessioned	2020-05-14T01:50:20Z
dc.date.available	2020-05-14T01:50:20Z
dc.date.issued	2019-10-01
dc.identifier.citation	International Journal of Structural Stability and Dynamics, 2019, 19, (10)
dc.identifier.issn	0219-4554
dc.identifier.issn	1793-6764
dc.identifier.uri	http://hdl.handle.net/10453/140703
dc.description.abstract	© 2019 World Scientific Publishing Company. In this paper, a new computationally efficient algorithm is developed for online and real-time identification of time, location, and severity of abrupt changes in structural stiffness as well as the unknown inputs such as earthquake signal. The proposed algorithm consists of three stages and is based on self-adaptive recursive least-square (RLS) and curvature-change approaches. In stage 1 (intact structure), a simple compact RLS is hired to estimate the unknown parameters and input of the structure such as stiffness and earthquake. Once the damage has occurred, its time and location are identified in stage 2, using two robust damage indices which are based on the structural jerk response and the error between measured and estimated responses of structure from RLS. Finally, the damage severity as well as the unknown excitations are identified in the third stage (damaged structure), using a self-adaptive multiple-forgetting-factor RLS. The method is validated through numerical and experimental case studies including linear and nonlinear buildings, a truss structure, and a three-story steel frame with different excitations and damage scenarios. Results show that the proposed algorithm can effectively identify the time-varying structural stiffness as well as unknown excitations with high computational efficiency, even when the measured data is contaminated with different levels of noise. In addition, as no optimization method is used here, it can be applied to real-time applications with computational efficiency.
dc.language	en
dc.publisher	World Scientific Pub Co Pte Lt
dc.relation.ispartof	International Journal of Structural Stability and Dynamics
dc.relation.isbasedon	10.1142/S0219455419501232
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering
dc.title	Real-Time Tracking of Structural Stiffness Reduction with Unknown Inputs, Using Self-Adaptive Recursive Least-Square and Curvature-Change Techniques
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-14T01:50:16Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	10

Abstract:

© 2019 World Scientific Publishing Company. In this paper, a new computationally efficient algorithm is developed for online and real-time identification of time, location, and severity of abrupt changes in structural stiffness as well as the unknown inputs such as earthquake signal. The proposed algorithm consists of three stages and is based on self-adaptive recursive least-square (RLS) and curvature-change approaches. In stage 1 (intact structure), a simple compact RLS is hired to estimate the unknown parameters and input of the structure such as stiffness and earthquake. Once the damage has occurred, its time and location are identified in stage 2, using two robust damage indices which are based on the structural jerk response and the error between measured and estimated responses of structure from RLS. Finally, the damage severity as well as the unknown excitations are identified in the third stage (damaged structure), using a self-adaptive multiple-forgetting-factor RLS. The method is validated through numerical and experimental case studies including linear and nonlinear buildings, a truss structure, and a three-story steel frame with different excitations and damage scenarios. Results show that the proposed algorithm can effectively identify the time-varying structural stiffness as well as unknown excitations with high computational efficiency, even when the measured data is contaminated with different levels of noise. In addition, as no optimization method is used here, it can be applied to real-time applications with computational efficiency.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/140703