Modal Identification of Civil Structures via Stochastic Subspace Algorithm with Monte Carlo–Based Stabilization Diagram
Publication: Journal of Structural Engineering
Volume 148, Issue 6
Abstract
The stochastic subspace algorithm is one of the most widely used structural identification techniques, which is generally involved with the stabilization diagram for estimating modal parameters. However, the conventional stabilization diagram has an inherent problem: some spurious modes may be identified as stable results, resulting in adverse effects on structural modal identification. To address this critical issue, this paper proposes an improved stochastic subspace algorithm involving a Monte Carlo–based stabilization diagram. Through a numerical simulation study, the good performance of the Monte Carlo–based stabilization diagram for discriminating the poles denoting the physical modes from those representing spurious modes is demonstrated. The numerical simulation results show that the proposed method can estimate structural modal parameters with high accuracy and robustness. Moreover, the proposed method is applied to field measurements on a 600-m-high skyscraper during Super Typhoon Mangkhut, and the results verify the applicability and effectiveness of the proposed method to field measurements. This paper aims to provide an effective tool for accurate estimation of modal parameters of civil structures.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published paper.
Acknowledgments
The work described in this paper was fully supported by a grant from the Research Grants Council of Hong Kong Special Administrative Region, China (Project No. CityU 11207519), a grant from the National Natural Science Foundation of China (Project No. 51778554), and a grant from City University of Hong Kong (Project No. 7005770).
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Published In
Journal of Structural Engineering
Volume 148 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 8, 2021
Accepted: Feb 1, 2022
Published online: Apr 8, 2022
Published in print: Jun 1, 2022
Discussion open until: Sep 8, 2022
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