Online automatic identification of the modal parameters of a long span arch bridge
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SSI-COV integrates seamlessly into a Monte Carlo framework; it is computationally efficient, robust, and well suited for automation, making it ideal for reproducible modal parameter extraction.
“In addition, SSI-COV integrates seamlessly into a Monte Carlo framework. It is computationally efficient, robust, and well suited for automation, making it ideal for reproducible modal parameter extraction [20,33,34,35].”
Damping ratios show increased sensitivity.
“Damping ratios also show increased sensitivity, consistent with prior observations [33].”
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- Authors
- Magalhães, F., Cunha, Á., & Caetano, E.
- Affiliations
- Universidade do Porto (PT)
- Corresponding author
- Magalhães, Filipe
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 23
- Issue
- 2
- Pages
- 316-329
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2068453352
- Cited by
- 506
- References
- 22
- Citations by year
- 2026: 31 · 2025: 63 · 2024: 51 · 2023: 63 · 2022: 48 · 2021: 31 · 2020: 35 · 2019: 30 · 2018: 24 · 2017: 27 · 2016: 17 · 2015: 17 · 2014: 13 · 2013: 12 · 2012: 18
- FWCI
- 30.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Mechanical Systems and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- modal parameter identification, long-span arch bridge, ambient vibration testing, covariance-driven stochastic subspace identification, stabilization diagram, hierarchical clustering, online structural health monitoring, dynamic monitoring system, damage detection, concrete arch bridge
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