# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
During the monitoring period, thirty-seven VIV events were observed and the stochastic subspace identification method was used to obtain the modal parameters from the measured data. It was found that the inhomogeneity of wind field along the bridge axis can affect VIV as an important factor. [PERSON] et al. [12] investigated the monitoring data of Hardanger Bridge, which is currently the longest suspension bridge in Norway. It found that the vortex shedding frequency of hangers is correlated to the 60 s mean wind velocity, and the possibility of identifying the VIV of hangers by analyzing the acceleration signal of deck is demonstrated. [PERSON] et al. [13] investigated the relationship between the amplitude of the VIV and the identified damping ratios based on the monitoring data of Jindo bridge. The multiple tuned mass dampers (MTMD) was then installed in the main span of the bridge to mitigate the vibration, and it was found that the effect of MTMD is gradually obvious when the wind velocity approaches the onset wind velocity of the VIV. 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; therefore, providing a powerful tool for verifying wind tunnel tests and further understanding of VIV. However, as VIV occurs only at special wind conditions, the majority of recorded acceleration responses are random vibrations caused by wind buffeting, passing vehicles, and other environmental effects. Only a few of them are vortex-induced vibrations due to periodic vortex shedding at specified wind conditions. In the previous studies [6; 7; 8; 9; 10], the VIV was selected out mainly by visual inspection of measured data. How to identify the VIV signal from the massive monitoring data, without manual intervention, becomes an important problem to be addressed. Wind buffeting is a multi-modal random vibration, while VIV is a single-mode, nearly harmonic vibration. Therefore, there are significant differences between the VIV and random vibration. Automatic identification aims at distinguishing between those two modes, and can be cast into the machine learning framework, such as classical classification problem or outlier detection. In the context of structural damage detection, a great amount of work has been conducted by using supervised and unsupervised learning algorithms for discriminating the damage state from the intact structure [16; 18; 21; 22]. [PERSON] et al. developed a damage detection algorithm that is based on statistical analysis of residual errors of fitted auto-regression models [16]. [PERSON] et al. suggested the damage detection to be addressed in the framework of outlier detection, especially in the case of low-level damage detection [21]. [PERSON] et al. developed a Bayesian non-parametric clustering approach for damage detection [23]. Unlike damage detection, VIV happens only at some specific wind conditions, and; therefore, studies addressing the automatic detection of VIV are rather limited. [PERSON] et al. [24] 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]. Their techniques are capable of identifying VIV events but also can discriminate different modes of vibration. However, in above studies the power spectral density of the acceleration response was taken as input, which may require a certain degree of manual intervention. Therefore, signal-based automatic identification of VIV events is an important and meaningful task.