Dynamic response prediction of high-speed train on cable-stayed bridge based on genetic algorithm and fused neural networks

Abstract

To predict the dynamic response of high-speed trains (HSTs) passing through cable-stayed bridges (CSBs), this paper proposed a prediction framework based on the genetic algorithm (GA) and the neural network. Firstly, an HST-CSB coupled dynamic model was established using the rigid-flexible coupling method. Subsequently, a fused Temporal Convolutional Network and Long Short-Term Memory (TCN-LSTM) neural network model was developed, and its hyperparameters were optimized using the GA. The dataset was created based on the HST-CSB coupled dynamic model for training and testing the model. Additionally, the generalization capability of the model after adopting different track deformations was discussed. The main results were as follows: Comparative analysis with various neural network models revealed that the GA-TCN-LSTM model exhibited the most accurate prediction results across different train speeds. The significant temperature fluctuations of the bridge led to a drastic deterioration of the track smoothness, resulting in decreased predictive performance of the neural network models. The GA-TCN-LSTM model could optimize the hyperparameters, demonstrating good robustness and generalization capabilities. The predictive performance of the model improved when using the German low-interference spectrum, attributed to the greater irregularities in the German low-interference spectrum, resulting in larger train dynamic responses and stronger feature characteristics in the training data for the neural network. Different neural network models exhibited varying adaptability to track irregularities, with the GA-TCN-LSTM model demonstrating high accuracy in predicting dynamic responses under different train speeds.