Nan Xu

Vortex-induced vibration (VIV) occurs frequently on long-span bridges at low wind speeds and may cause fatigue damage and serviceability problems, so its reliable identification from field monitoring data is an important engineering task. However, existing automated identification methods rely mainly on single-point vibration indicators, and recognizing VIV events in massive, unlabeled monitoring datasets remains challenging. This study proposes an unsupervised VIV identification method based on a newly proposed feature, the Two-Point Vibration Phase Difference (TPVPD), computed between the vertical accelerations measured at the 1/4- and 3/4-span of the bridge. Because a VIV lock-in response is dominated by a single vibration mode, the phase difference between two spanwise locations remains nearly constant in time, whereas it fluctuates strongly under buffeting. The method was validated using six years (2013–2018) of monitoring data from a 1650-m main-span suspension bridge, covering 2.85 × 10⁵ valid 10-min windows. Spectral clustering of three features—acceleration RMS, TPVPD standard deviation, and an energy concentration coefficient—separated VIV from non-VIV states with a window-level F1 score of 0.84 with respect to a reference VIV inventory, and 99.8% of the windows identified as VIV exhibited spectral peaks at one of the six known VIV mode frequencies of the bridge. A rule-based cluster assignment calibrated on previous years transferred to subsequent years with F1 scores of 0.74–0.88, indicating the feasibility of operational deployment.

Vortex-induced vibration (VIV) of long-span bridges can be of large amplitude, which can influence serviceability. Therefore, it is important to predict the response of vortex-induced vibration to aid the management of long-span bridges. A novel data-driven model is proposed to predict the time history of the dynamic response of VIV events. Specifically, the proposed model consists of gated recurrent unit (GRU) neural networks and the Newmark-beta method. GRU neural networks can perform accurate sequential prediction, and the Newmark-beta method can complement the physical meaning of the middle output of the proposed model. To aid the accurate prediction of the amplitude of VIV events, the proposed model employs weighted mean square error as the loss function, which can put more emphasis on the amplitude. The proposed model is validated on measured VIV events of a long-span suspension bridge. The weighted mean absolute percentage error and Pearson correlation coefficient of the trained model indicate the effectiveness of the proposed model.