Vibration monitoring and wind-train coupling index-integrated machine learning for condition identification of adjacent high-speed railway transmission towers

When high-speed railway lines are located close to transmission corridors, train-induced vibration may affect the dynamic response of transmission towers. To investigate the event-level vibration response characteristics of a transmission tower adjacent to a high-speed railway under short-term field monitoring conditions, this study takes a transmission tower in Shenzhen, China, as the research object. Train passage events were constructed and analyzed using horizontal acceleration, inclination, background wind speed, and train timetable data. First, train events and single- or double-train conditions were identified according to the timetable. Then, the east-west and north-south acceleration components were used to calculate the resultant horizontal acceleration, and the peak response time within each recorded event minute was determined using a 1 s moving root-mean-square method. Finally, a 20 s event window, including 10 s before and 10 s after the peak response time, was established to extract event-level features related to acceleration, inclination, wind speed, and train type. The results show that 226 actual train events were obtained from November 6 to November 11, 2025, of which 170 non-boundary events were used as the main analysis samples. The maximum RMS response and peak acceleration of double-train events were generally higher than those of single-train events, although the differences were limited. The acceleration response in the higher wind-speed group was also generally greater than that in the lower wind-speed group. However, because the wind-speed groups were partly coupled with the monitoring dates, this result should be interpreted as a tendency rather than a causal conclusion. Furthermore, response state classification was conducted based on the event-level features. The random forest model achieved relatively good identification performance, with an F1-score of 0.788 and a ROC-AUC of 0.854. Feature importance analysis indicated that maximum inclination variation, mean wind speed, and peak offset time made relatively large contributions to response state identification. The results provide a reference for short-term monitoring data analysis and event-level response assessment of transmission towers adjacent to high-speed railways.