# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 4 Conclusions  
This paper takes machine learning 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 on TBM penetration analysis and prediction. Based on the field data, this paper analyzes and summarizes the influencing factors of the TBM performance of the deep-buried tunnel and uses the relevant methods of machine learning to establish the prediction model of the TBM penetration rate of the deep-buried tunnel. The main conclusions are as follows.  
1. Based on the establishment of the database of TBM performance in the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, the factors influencing TBM performance are quantitatively analyzed, including TBM mechanical parameters (cutterhead thrust (TF), cutterhead torque (T), cutterhead speed (RPM), etc.) and rock mass parameters (uniaxial compressive strength (UCS), the number of rock volume joints (\(J_{\text{v}}\)), angle between joint surface and tunnel axis (\(\alpha\)), etc.).
2. Since there are many influencing factors on the TBM penetration rate, and the current situation is that the input parameters of the prediction model are determined artificially and the subjectivity is strong, the random forest (RF) algorithm is used to select the characteristics of the influencing factors on the TBM penetration rate, and the weight of the influencing factors is sorted to obtain the parameters of cutterhead thrust (TF), the number of rock volume joints (\(J_{\text{v}}\)), uniaxial compressive strength (UCS) and rotational speed (RPM). The total weight is 75.99%, which is comprised of the most important four parameters affecting the TBM penetration rate. The input parameters are determined for the TBM penetration rate prediction model based on machine learning.
3. The prediction model of the TBM penetration rate based on multiple regression (MR) is established. The penetration index (FPI) is used as a bridge to connect the rock mass parameters and TBM mechanical parameters. The empirical formula of the TBM penetration rate prediction with independent variables of cutterhead thrust (TF), the number of rock volume joints (\(J_{\text{v}}\)), uniaxial compressive strength (UCS) and rotational speed (RPM) is obtained. The empirical formula can be applied to the preliminary prediction of the TBM penetration rate in the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project.
4. Based on the input parameters selected by the random forest algorithm, the prediction models of the TBM penetration rate are established based on the BP neural network (BPNN) and support vector regression (SVR). After training the training set and setting the parameters on the test set, the mean absolute percentage error (MAPE) and mean square error (MSE) of the BPNN prediction model are 22.95% and 0.3445, respectively. The mean absolute percentage error (MAPE) and mean square error (MSE) of the SVR model are 26.59% and 0.4085, respectively.
5. Through the comparative analysis of the three prediction models, the BPNN prediction model shows better prediction performance and generalization ability than the multiple regression model and SVR model, which are embodied in higher prediction accuracy and stability.  
Writing--original draft, T.M., Y.J., Z.L. and Y.K.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research Development Plan (No. 2018 YFC1505301) and the Chinese National Natural Science Foundation (No. 41941018).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.