Time-Domain Feature-Based Anomaly Detection of Extreme Vibration Events for Cross-River Bridge Piers

This study proposes a time-domain feature-based anomaly detection method for vibration data of bridge piers collected by underwater seismometers operating under alternating submerged and exposed conditions. The method aims to accurately identify anomalies under both normal and extreme events. Taking the Fuzhou Pushang Bridge as a case study, the acceleration root mean square (aRMS) is adopted as the representative vibration feature to investigate the effects of vehicular loads, water level variations, and tidal fluctuations. The results show that pier vibrations are primarily dominated by vehicular loads, exhibiting pronounced daily periodicity, intraday non-stationarity, and non-normality, while the influences of water level and tidal variations are relatively minor. Based on these characteristics, an anomaly detection framework integrating STL decomposition (Seasonal-trend decomposition using Loess), Yeo–Johnson transformation, and control charts is developed. Historical data are used to establish control limits and conduct self-validation, yielding an anomaly rate of 0.14%, which is consistent with the theoretical probability of ±3σ control limits. When applied to the subsequent monitoring period, the anomaly rate under normal conditions is 0.18%, demonstrating the stability of the proposed method. Further analysis reveals that anomalies are primarily caused by direct hydrodynamic impacts on the instrument. Under flood conditions, continuous anomalies occur during nighttime, with the anomaly rate increasing to 4.44%. Under seismic conditions, the control chart statistic reaches 5.03, significantly exceeding the control limits. Comparative analysis shows that the percentile-based method yields a higher anomaly rate (0.65%), indicating a higher false alarm rate. Overall, the proposed method demonstrates strong generalization capability and reliability, providing effective support for long-term structural health monitoring of bridge substructures in complex environments.