Continuous Modal Identification and Tracking of a Long-Span Suspension Bridge Using a Robust Mixed-Clustering Method
Publication: Journal of Bridge Engineering
Volume 27, Issue 3
Abstract
For long-term continuous structural health monitoring to be effective, a process of continuous modal identification that should preferably be automated is required. This case study paper describes a robust, fully automated approach for continuous modal identification and tracking. The approach is demonstrated on a long-span suspension bridge under operational conditions. Contributions are made in three stages: eliminating the spurious modes, extracting the physical modes, and estimating the precision. The proposed approach helps avoid any manual intervention, requires no manually tuned thresholds or prior assumption, and is robust. One week of field monitoring data are analyzed to validate the process. Modal tracking is conducted to show the stability of continuous analysis and to track the evolution of modal parameters. Parametric analysis is conducted to demonstrate the robustness. The case study shows that the proposed approach yields better results than alternative approaches and successfully identifies and tracks multiple closely spaced modes without any manual intervention.
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Acknowledgments
This work was supported by the National Key R&D Program of China (Grant nos. 2018YFB16003001 and 2019YFB1600702) and the National Natural Science Foundation of China (Grant no. 51878059). The first author also thanks the China Scholarship Council for its financial support.
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Information & Authors
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Published In
Journal of Bridge Engineering
Volume 27 • Issue 3 • March 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 10, 2021
Accepted: Nov 23, 2021
Published online: Jan 6, 2022
Published in print: Mar 1, 2022
Discussion open until: Jun 6, 2022
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