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Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions

Wang, Xiaoyou and Li, Lingfang and Beck, James L. and Xia, Yong (2021) Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions. Mechanical Systems and Signal Processing, 154 . Art. No. 107563. ISSN 0888-3270. doi:10.1016/j.ymssp.2020.107563.

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Damage detection of civil engineering structures needs to consider the effect of normal environmental variations on structural dynamic properties. This study develops a novel structural damage detection method using factor analysis in the sparse Bayesian learning framework. The unknown changing environmental factors that affect the structural dynamic properties are treated as latent variables in the model. The automatic relevance determination prior is adopted for the factor loading matrix for model selection. All variables and parameters, including the factor loading matrix, error vector and latent variables, are solved using the iterative expectation-maximization technique. The variables are then used to reconstruct structural responses. The Euclidean norm of the error vector is calculated as the damage indicator to detect possible damage when limited vibration data are available. Two laboratory-tested examples are utilized to verify the effectiveness of the proposed method. Results demonstrate that the number of underlying environmental factors and structural damage can be accurately identified, even though the changing environmental data are unavailable. The proposed method has the advantages of online monitoring and automatic identification of underlying environmental factors.

Item Type:Article
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Wang, Xiaoyou0000-0002-4588-2460
Additional Information:© 2020 Elsevier Ltd. Received 19 August 2020, Revised 9 December 2020, Accepted 20 December 2020, Available online 15 January 2021. The research in this paper was supported by the Key-Area Research and Development Program of Guangdong Province (Project No. 2019B111106001), RGC-GRF (Project No. 15201920) and PolyU Project of Strategic Importance (Project No. 1-ZE1F). CRediT authorship contribution statement: Xiaoyou Wang: Conceptualization, Methodology, Formal analysis, Writing - original draft. Lingfang Li: Data curation. James L. Beck: Writing - review & editing. Yong Xia: Writing - review & editing, Supervision, Project administration, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding AgencyGrant Number
Key-Area Research and Development Program of Guangdong Province2019B111106001
Research Grants Council of Hong Kong15201920
PolyU Project of Strategic Importance1-ZE1F
Subject Keywords:Structural damage detection; Sparse Bayesian learning; Factor analysis; Environmental variations; Automatic relevance determination
Record Number:CaltechAUTHORS:20210311-132657694
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Official Citation:Xiaoyou Wang, Lingfang Li, James L. Beck, Yong Xia, Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions, Mechanical Systems and Signal Processing, Volume 154, 2021, 107563, ISSN 0888-3270,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108402
Deposited By: Tony Diaz
Deposited On:12 Mar 2021 19:09
Last Modified:16 Nov 2021 19:11

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