Structural Prediction Analysis of Cross-Sea Cable-Stayed Bridges during Operation Based on Existing Traffic Volumes

Abstract

In order to study the safety state of the structure of a cross-sea cable-stayed bridge during its operation period, this paper proposes a combined long-term traffic prediction model based on the XGBoost (eXtreme Gradient Boosting) model and LSTM (Long Short Term Memory) model in the context of a cross-sea cable-stayed bridge in Qingdao. XGBoost is an optimized distributed gradient enhancement library. LSTM is a neural network for processing long sequence data. The LSTM model and the XGBoost model were first built separately, and then a genetic algorithm was used to select the optimal weight parameters to combine the two models for prediction. Based on the traffic prediction results of the combined LSTM-XGBoost model, a finite element model was established using numerical analysis. The effect of different traffic volumes on the deflection and stresses in the span of the main beam and the stresses in the diagonal cables was analyzed using the time course analysis method. From the point of view of structural safety, the maximum of future traffic limits and more reasonable average traffic speeds are given to provide a basis for the later management of the bridge.