A rapid prediction method of seismic-induced damage in high-speed railway ballastless track simply-supported bridge system is proposed based on convolutional neural networks.To obtain more information of seismic motion,one-dimensional seismic motion data is transformed into three-dimensional image through continuous wavelet transform as the input of convolutional neural network.The reliability of the proposed method is validated by comparing with results in damage samples database.The influence of different hyperparameters of convolutional neural networks on prediction results and training duration are analyzed,and a combination of hyperparameters of convolutional neural networks optimized by Bayesian optimization is obtained.The time required for seismic analysis of high-speed railway ballastless track simply-supported bridge system using different seismic analysis methods is compared.The optimized convolutional neural network is utilized to predict seismic-induced damage of different key components in high-speed railway ballastless track simply-supported bridge system.The research indicates that the initial learning rate is the most significant factor affecting the accuracy of network prediction,while the learning rate decay factor,batch size,and number of training epochs have certain effects on the network prediction results.The training duration of convolutional neural network is mainly determined by the number of training epochs and batch size.The proposed method demonstrates high prediction accuracy for seismic-induced damage in various components of high-speed railway ballastless track simply-supported bridge system,and the network structure exhibits high applicability.The optimized convolutional neural network has shorter training time and more accurate prediction for seismic-induced damage in high-speed railway ballastless track simply-supported bridge system.The research findings can provide reference for rapid repair of seismic-induced damage in high-speed railway systems after earthquakes.
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