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.
In mountainous areas, more challenges are expected for the construction of long-span bridges. The flutter instability during erection is an outstanding issue due to flexible structural characteristics and strong winds with large angles of attack. Taking the suspension bridge as an example, the flutter stability of the bridge with different suspending sequences was investigated. First, the dynamic characteristics of the bridge during erection were computed by the finite element software ANSYS, along with the effects on flutter stability discussed. Then, different aerodynamic shapes of the bridge girder during erection were considered. The aerodynamic coefficients and the critical flutter state were determined by wind tunnel tests. Based on the above analysis, some structural measures are proposed for improving the flutter stability of the bridge during erection. The results show that the flutter stability of the bridge during erection is related to the suspending sequence and the aerodynamic shape of the girder. Owing to the structural dynamic characteristics, the bridge has better flutter stability when the girder segments are suspended symmetrically from the two towers to the mid-span. Considering the construction requirement that the bridge deck should be laid without intervals, this structural superiority is seriously weakened by the unfavorable aerodynamic shape of the girder. In order to improve the flutter stability of the bridge during erection, an effective way is to adopt some temporary structural strengthening measures.
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