Rock mass classification method of TBM tunnel face based on driving parameters

Rock mass classification of TBM tunnel is an important reference index to analyze the TBM performance and determine the support mode. It is of great significance for the safe and efficient construction of tunnel to quickly recognize the rock mass classes of tunnel face. In this paper, the data preprocessing is carried out by statistical method, and the input eigenvalues of model are selected through correlation analysis. A LIBSVM rock mass classification model is established, with the penetration rate, thrust and cutterhead torque as the model inputs, and the rock mass classes obtained by the Hydropower Classification (HC) method as the model output. The test results show that the average precision of LIBSVM model is 0.919, and the recall rate of Class II rock mass is as high as 0.980. The data overlap of rock mass of Class III, IV and V is an important factor affecting the precision of the model. Analyzing the importance of the 6 input eigenvalues of model in each two-classifier, the results show that the mean value of the penetration rate, the thrust and the cutterhead torque play a major role, and the standard deviations of their variation play a supplementary role.