Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures
Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. The authors introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. The results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14759217
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Supplemental Notes:
- © The Authors 2021.
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Authors:
- Zhang, Erhua
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0000-0003-3237-5993
- Wu, Di
- Shan, Deshan
- Publication Date: 2022-7
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1541-1554
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Serial:
- Structural Health Monitoring
- Volume: 21
- Issue Number: 4
- Publisher: Sage Publications, Incorporated
- ISSN: 1475-9217
- EISSN: 1741-3168
- Serial URL: http://shm.sagepub.com/
Subject/Index Terms
- TRT Terms: Algorithms; Cable stayed bridges; Mathematical models; Modal analysis; Tensor analysis
- Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;
Filing Info
- Accession Number: 01856365
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 29 2022 9:27AM