Estimating stay cable vibration under typhoon with an explainable ensemble learning model

Excessive vibration of stay cables in strong winds has been a concern for bridge operators, which impairs the durability of both the cables and the bridge structure. This paper develops a data-driven approach to predict the amplitude of the cable vibration using an ensemble learning model. The model aims to predict cable vibrations in both in-plane and out-of-plane directions, with the wind speed, wind direction, turbulence intensity, and deck acceleration as input variables. Especially, the deck acceleration is included considering the deck-cable interaction and vehicle effects, which significantly improved the accuracy of the prediction. Furthermore, the model is interpreted with local interpretable model-agnostic explanations (LIME) and partial dependence plot (PDP) methods. The former demonstrates the relative importance of input variables on a global scale, and the latter indicates the correlation between individual input variables with the prediction target. The investigation is validated using the data harnessed from structural health monitoring (SHM) of a 1088-m cable-stayed bridge during three typhoon events. The adopted Gradient boosting regression tree (GBRT) model demonstrated better performance than other state-of-the-art machine learning models. The developed approach can provide guidance on preventive maintenance of stay cables to avoid damage due to excessive vibration.

Automated summary

This paper develops a data-driven approach to predict the amplitude of stay cable vibration in strong winds. The model uses an ensemble learning model and considers wind speed, wind direction, turbulence intensity, and deck acceleration as input variables. The deck acceleration, which takes into account the deck-cable interaction and vehicle effects, significantly improves the accuracy of the prediction. The approach is validated using data from structural health monitoring of a cable-stayed bridge during three typhoon events.