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In this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with the time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in three typical cases, the wave-load-on-deck under regular waves, structural displacement under combined wind and wave loads, and wave height variation along with typhoon/hurricane approaching. To enhance the prediction accuracy, a typical data preprocessing method is adopted and an improved prediction framework for the LSTM model after the rolling forecast prediction is proposed. The obtained results show that: (a) When making a prediction on data featured with periodic regularity, both the XGBoost and ARIMA models perform well, and the XGBoost model can make predictions multi-step ahead, (b) The ARIMA model can predict just one step ahead based on aperiodic dataset with limited amplitude more accurately, while the XGBoost and LSTM models can predict multi-step ahead with appropriate data preprocessing, and (c) All the three models can predict the data tendency with model updating over time, but the prediction accuracy of the LSTM model is more favorable. The successful application of these three machine learning techniques can provide guidance to resolve engineering problems with time-history prediction requirements.

Application of time series prediction techniques for coastal bridge engineering