Abstract
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.
Highlights
More intensive economic activities in coastal zones trigger the necessity of constructing more long and flexible coastal bridges that usually cross vast and deep water
This study aims to address the particular features of the major loads and structural dynamics for coastal bridges by using three competitive time series prediction techniques, the Extreme Gradient Boosting (XGBoost), Long Short-Term Memory Networks (LSTM), and Autoregressive Integrated Moving Average Model (ARIMA)
The two coefficients can be roughly estimated by observing the graph of ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function), precisely determined by grid search, information criterion function, thermodynamic diagram or other methods
Summary
More intensive economic activities in coastal zones trigger the necessity of constructing more long and flexible coastal bridges that usually cross vast and deep water. To address the structural safety and resilience for coastal bridges under various extreme environmental conditions, quick and accurate prediction of the major loads and structural dynamic responses in advance would be highly desirable, especially for the stakeholders to make expedient decisions on the evacuation route before a hurricane landing. There are rare studies on using time series prediction techniques for estimating the response of bridges under dynamic loads in coastal environment, which is essential in terms of the hazard prevention for coastal bridges. This study aims to address the particular features of the major loads and structural dynamics for coastal bridges by using three competitive time series prediction techniques, the XGBoost, LSTM, and ARIMA. The features of the statistical data sets associated with coastal bridges are representative and this study can provide guidance to resolve similar engineering problems with time-history prediction requirements
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