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

Abstract

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.

Keywords:

• Cable-stayed bridge
• cable vibration
• ensemble machine learning
• GBRT model
• local interpretable model-agnostic explanations
• partial dependence plot
• structural health monitoring
• typhoon event

Additional information

Funding

The authors would like to acknowledge financial supports from the National Natural Science Foundation of China (Grant No. 52178306), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR19E080002), and Portuguese national funds through the FCT/MCTES (PIDDAC) under the project EXPL/ECI-EGC/1324/2021. The kind support from the operator of the case-study bridge is also acknowledged. The conclusions and opinions in this paper are of the authors, which do not necessarily reflect that of the bridge operator.