[Objective]In order to strengthen the status ma-nagement of urban rail transit line sections,it is necessary to predict the overall deterioration trend of the gauge in space.[Method]CEEMD(complementary ensemble empirical mode decomposition)theory is introduced to extract the IMF(intrin-sic mode function)of the geometric alignment of the track sec-tion.The PSO(particle swarm optimization)algorithm is uti-lized to optimize the SVR(support vector regression machine)to train and test the extracted data after calibrating the optimal parameters of the prediction model.Thus,the CEEMD-PSO-SVR prediction model is constructed.The prediction model is tested with 1,128 sets of track inspection sample data within the upward track section from K1 2+134 to K1 5+743 on Shanghai Metro Line 16.[Result & Conclusion]Compared with the PSO-SVR model and the ARIMA(autoregressive in-tegrated moving average)model,the CEEMD-PSO-SVR pre-diction model has advantages in three performance evaluation indicators,namely root mean square error,mean absolute er-ror,and absolute value of mean relative error.
维普
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