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Title: Bayesian operational modal analysis of structures with tuned mass damper
Authors: Wang, Xinrui
Zhu, Zuo
Au, Siu-Kui
Keywords: Engineering::Civil engineering
Issue Date: 2023
Source: Wang, X., Zhu, Z. & Au, S. (2023). Bayesian operational modal analysis of structures with tuned mass damper. Mechanical Systems and Signal Processing, 182, 109511-.
Project: SUG/4(C120032000)
Journal: Mechanical Systems and Signal Processing
Abstract: Tuned mass damper (TMD) is a common strategy to reduce structural vibration in a passive manner without the need for active power. The basic parameters of a TMD include its mass ratio, natural frequency and damping ratio. While these parameters are factory-calibrated before installation, it would be desirable to assess the in-situ properties of the TMD and the ‘primary’ structure under operational state, e.g., to validate/assess performance and detect detuning over the service life. In this work, a Bayesian approach is developed for identifying the modal parameters of the TMD and primary structure using only the ambient vibration data measured on the primary structure, i.e., ‘operational modal analysis’. The likelihood function and theoretical PSD matrix of ambient data are formulated, accounting for primary-secondary structure dynamics with non-classical damping that is not treated in existing Bayesian formulations. An Expectation- Maximisation (EM) algorithm is developed for efficient computation of the most probable value of modal parameters. Analytical expressions are derived so that the ‘posterior’ (i.e., given data) covariance matrix can be determined accurately and efficiently. The proposed method is verified using synthetic data and applied to field data of a chimney with close modes response attenuated by a TMD.
ISSN: 0888-3270
DOI: 10.1016/j.ymssp.2022.109511
Schools: School of Civil and Environmental Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved. This paper was published in Mechanical Systems and Signal Processing and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20250201
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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  Until 2025-02-01
Accepted manuscript1.27 MBAdobe PDFUnder embargo until Feb 01, 2025

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