Classification Method for Railway Tunnel Secondary Lining Cold Joint Detection based on CNN-BiLSTM-SVM Model with Improved Hybrid Leader Algorithm

Abstract

Cold joints pose great safety risks to the safe operation of railways. In view of the existing cold joint detection methods, which have low detection efficiency and difficulty in data analysis, a tunnel secondary lining cold joint detection classification method based on the improved hybrid leader CNN-BiLSTM-SVM model is proposed. First, the Rayleigh wave method is used to extract the waveform information of the cold joints. Secondly, CNN-BILSTM is used to perform feature extraction and fusion processing on the waveform information and then input into the support vector machine, and the improved hybrid leader algorithm is used to optimize the parameters in the SVM. Finally, the information is input into the optimized CNN-BiLSTM-SVM to obtain the cold joints detection classification results. In order to verify the effectiveness of this method, the waveform data collected using the Rayleigh wave method in the tunnel under construction and the verified coring detection results are used as the data set. The results show that the results of this method are higher than the unoptimized CNN-BILSTM-SVM and the CNN-BILSTM-SVM optimized by the seagull optimization algorithm and the sparrow search optimization algorithm.