Vortex-Induced Vibration Identification for Long-Span Bridges: A Method Using Two-Point Vibration Phase Difference with Machine Learning

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.