Abstract
Vortex-induced vibration (VIV) has been occasionally observed on a few long-span steel box-girder suspension bridges. The underlying mechanism of VIV is very complicated and reliable theoretical methods for prediction of VIV have not been established yet. Structural health monitoring (SHM) technology can provide a large amount of data for further understanding of VIV. Automatic identification of VIV events from massive, continuous long-term monitoring data is a non-trivial task. In this study, a method based on the random decrement technique (RDT) is proposed to identify the VIV response automatically from the massive acceleration response without manual intervention. The raw acceleration data is first processed by RDT and it is found that the RDT-processed data show different characteristics for the VIV response and conventional random response. A threshold based on the coefficient of variation (COV) of peak values of processed data is defined to distinguish between the two kinds of responses. Both random vibration and VIV for a three-DOF (degree-of-freedom) mass-spring-damper system are obtained by numerical simulation to verify the proposed method. The method is finally applied to the Xihoumen suspension bridge for identifying VIV response from three-month monitoring data. It is shown that the proposed method performs comparably with the method of novelty detection. A total of 60 VIV events have been successfully identified. Vortex-induced vibrations for the second to ninth vertical modes with modal frequency within 0.1~0.5 Hz occurs at wind velocity 5–18 m/s, with wind direction nearly perpendicular to bridge axis. Amplitude of VIV generally decreases with increase of wind turbulence intensity; however, noticeable VIV amplitude are still observed for turbulence intensity up to 13% in some cases.
Highlights
Due to their high flexibility and low structural damping, long-span cable-supported bridges are susceptible to a variety of wind-induced vibrations [1,2,3,4]
Proposed a technique based on cluster analysis to identify vortex-induced vibration (VIV) events from long-term monitoring data, where the power spectral density of acceleration response is taken as feature of cluster analysis, and that method is applied to the Xihoumen Bridge
In recognition of the difference, this paper proposes an automatic identification method for VIV based on random decrement technique
Summary
Due to their high flexibility and low structural damping, long-span cable-supported bridges are susceptible to a variety of wind-induced vibrations [1,2,3,4]. Long-term structural health monitoring has increasingly become an important technique for health monitoring and condition assessment of bridges [14,15,16,17,18,19,20] It records a large amount of data of bridges when subjected to vortex-induced vibrations; providing a powerful tool for verifying wind tunnel tests and further understanding of VIV. Proposed a technique based on cluster analysis to identify VIV events from long-term monitoring data, where the power spectral density of acceleration response is taken as feature of cluster analysis, and that method is applied to the Xihoumen Bridge. Later they developed a decision tree method to classify the mode branch of VIV [25].
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