# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 1 Introduction
Combined with the relevant data of the TBM cutterhead crushing stage in the TBM section of the Jilin Yinsong Project, the prediction model of the TBM penetration rate was established with uniaxial compressive strength (UCS), intactness index of rock mass (\(K_{\text{v}}\)) and cutterhead thrust (\(F\)) as parameters. With the more and more extensive application of the TBM, TBM related research has entered the era of big data. With the continuous development of machine learning theory and the emergence of 5G technology, the application of machine learning in TBM tunneling has a broad prospect. [PERSON] et al. [12] established the prediction model of rock mass parameters during TBM excavation by using the intelligent algorithm of single target stacking (SST) and improved support vector regression (SVR). The model was based on 180 sets of data sets of the Jilin Yinsong Project. The improved SVR algorithm showed higher prediction accuracy for rock mass parameters such as uniaxial compressive strength (UCS) and the rock mass britteness index (BI). [PERSON] et al. [13] proposed to use the multiple adaptive regression spline method (MARS) to predict the maximum ground settlement of the earth pressure balance shield tunnel. The model was established by using 148 sets of data from three independent earth pressure balance shield tunnel projects in Singapore. Compared with other methods such as the artificial neural network (ANN) and relevance vector machine (RVM), the model is simple to operate and easy to calculate the input rate of target contribution. [PERSON] et al. [14] developed a prediction model of the TBM jamming risk based on the Bayesian network (BN) and error back propagation neural network (BPNN) algorithm to predict the jamming risk index (\(J_{\text{r}}\)) to evaluate the design thrust value more reasonably in the design stage of TBM equipment. [PERSON] et al. [15] developed the intelligent control system of TBM tunneling parameters, which uses the combination of artificial neural network (ANN), support vector machine (SVM) and a least square regression to realize the prediction of TBM tunneling parameters. The system has been preliminarily applied in the TBM construction of the Jilin Yinsong Project and has certain feasibility. In this paper, machine learning is used as the main technical means, relying on the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project to carry out the relevant research. Based on a theoretical analysis and on-site actual data, the relevant methods of machine learning are used to develop the TBM tunneling efficiency prediction model of the deep-buried tunnel. By establishing the database of TBM performance in the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, the TBM mechanical parameters (thrust of cutterhead, torque of cutterhead, cutting speed, etc.) and rock mass parameters (uniaxial compressive strength, number of unit volumetric joints, angle between joint surface and tunnel axis, etc.) which affect TBM tunneling efficiency are quantitatively analyzed. Based on the quantitative analysis, the main influencing factors of TBM driving efficiency are obtained. After that, there are many influencing factors of the TBM penetration rate in the database of TBM performance. The random forest (RF) algorithm is used to select the characteristics of the influencing factors of the TBM penetration rate, and the factors that have a great influence on the TBM penetration rate are screened out. The input parameters are determined for the TBM penetration rate prediction model based on machine learning. Finally, based on the input parameters analyzed above, three prediction models of TBM penetration rate are established, which are multiple regression (MR), BP neural network (BPNN) and support vector regression (SVR). The prediction effect of the model is evaluated by the mean square error of the model and the absolute error and relative error of the prediction samples. It is concluded that the BPNN model has the best prediction effect.