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Journal of Zhejiang University SCIENCE A 1998 Vol.-1 No.-1 P.


Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge

Author(s):  Yan-Long XIE, Bin-bin LI, Jian GUO

Affiliation(s):  ZJU-UIUC institute, Zhejiang University, Haining 314400, China; more

Corresponding email(s):   guoj@zjut.edu.cn

Key Words:  Cable-stayed sea-crossing bridge, Operational modal analysis, Bayesian modal identification, EM algorithm

Yan-Long XIE, Bin-bin LI, Jian GUO. Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .

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author="Yan-Long XIE, Bin-bin LI, Jian GUO",
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%T Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge
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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, 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 of long-span and flexible to have extremely low natural frequencies. It challenges experimental capability (e.g., instrumentation, 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+620+218 meters. The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean. Ambient vibration data was collected for 24 hours. A Bayesian FFT modal identification method incorporating an expectation-maximization (EM) algorithm is applied for modal analysis, in which the modal parameters and associated identification uncertainties are both addressed. Nineteen modes, including fifteen translational modes and four torsional modes, are identified within the frequency range of [0, 2.5 Hz].

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