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Journal of Zhejiang University SCIENCE A
ISSN 1673-565X(Print), 1862-1775(Online), Monthly
2020 Vol.21 No.7 P.553-564
Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge
Abstract: Sea-crossing bridges have attracted considerable attention in recent years as an increasing number of projects have been constructed worldwide. Situated in the coastal area, sea-crossing bridges are subjected to a harsh environment (e.g. strong winds, possible ship collisions, and tidal waves) and their performance can deteriorate quickly and severely. To enhance safety and serviceability, it is a routine process to conduct vibration tests to identify modal properties (e.g. natural frequencies, damping ratios, and mode shapes) and to monitor their long-term variation for the purpose of early-damage alert. Operational modal analysis (OMA) provides a feasible way to investigate the modal properties even when the cross-sea bridges are in their operation condition. In this study, we focus on the OMA of cable-stayed bridges, because they are usually long-span and flexible to have extremely low natural frequencies. It challenges experimental capability (e.g. instrumentation and budgeting) and modal identification techniques (e.g. low frequency and closely spaced modes). This paper presents a modal survey of a cable-stayed sea-crossing bridge spanning 218 m+620 m+218 m. The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean. Ambient vibration data was collected for 24 h. A Bayesian fast Fourier transform modal identification method incorporating an expectation-maximization algorithm is applied for modal analysis, in which the modal parameters and associated identification uncertainties are both addressed. Nineteen modes, including 15 translational modes and four torsional modes, are identified within the frequency range of [0, 2.5 Hz].
Key words: Cable-stayed sea-crossing bridge; Operational modal analysis (OMA); Bayesian modal identification; Expectation-maximization (EM) algorithm
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DOI:
10.1631/jzus.A1900511
CLC number:
U441.3
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On-line Access:
2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2020-06-15