High-speed railway seismic response prediction using CNN-LSTM hybrid neural network
This paper proposes a hybrid model prediction method based on convolutional neural network(CNN)and long short-term memory network(LSTM)for the characteristics of time-series and nonlinearity of earthquake response data to better exploit the response information of high-speed railroads during the earthquake and improve the efficiency and prediction accuracy of fiber grating monitoring.Seven gratings are arranged on the fiber by laying quasi-distributed fiber optic gratings on the shaking table of the high-speed railroad simple beam to collect the response data of the track plate,rail,base plate,and beam during the earthquake,and the response data of the two side gratings are used to predict the grating response of the middle point,and the continuous feature map is constructed as the input by the time-sliding window of the acquisition location,historical data,and seismic.The CNN is utilized first to extract the feature vector,which is then produced and used as the input data of the LSTM network in a time-series sequence,and ulti-mately,the LSTM network is used for prediction.The experimental findings reveal that the LSTM network performs best at three layers,and the CNN-LSTM approach has a good prediction accuracy with RRMSE,RMAE,R2 at 0.375 3,0.296 8,and 0.937 1,respectively.
quasi-distributed fiber Bragg gratingthe shaker testseismic responseCNN-LSTM hybrid network model
万方数据
维普
