# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Technique**} & \multicolumn{2}{c}{**Parameters**} & \multirow{2}{*}{**Datasets/Description**} \\ \cline{3-4}  & & **Input** & & **Output** \\ \hline [PERSON] [19] & ANN & CFF, Dc, RPM, TE, UCS & AR, PR & 640 TBM projects \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [36]} & \multirow{4}{*}{ANN} & N, overburden, permeability, RMR, rock mass weathering, RQD, UCS, WTS, & AR & Athens metro tunnel \\ \cline{1-1} \cline{2-4}  & & BI, DPW, UCS, \(\alpha\) & PR & 151 datasets \\ \hline \multirow{2}{*}{[PERSON] et al. [39]} & \multirow{2}{*}{ANN} & BTS, CT, Qu, RMR, Rock Type, RQD, Rs TF, UCS & PR & 10 km data excavated in a tunnel \\ \cline{1-1} \cline{2-4}  & & & & \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [40]} & \multirow{2}{*}{ANN} & DPW, RQD, UCS & PR & 185 datasets \\ \cline{1-1} \cline{2-4}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Published research on tunnel boring machine (TBM) performance prediction using related artificial intelligence (AI) and machine learning (ML) techniques. While the use of AI techniques has attracted more attention than the use of other techniques, such as ML, in the field of tunneling the main aim of AI is the development of reliable and robust models. On the other hand, ML techniques seek to enhance accuracy, which is desirable for any prediction model. Since the TBM PR is a continuous target variable, the supervised ML techniques, such as SVM, nearest neighbor and decision trees (DTs) should be used for classification and regression of TBM data. Supervised ML approaches are powerful due to their ability to solve problems in science and engineering and obtaining a high accuracy level for prediction purposes, especially when dealing with highly complex and nonlinear problems [48; 49; 50]. This study aims to evaluate the use of various ML techniques in predicting the TBM PR. In this study, five ML techniques are developed to predict PR of TBM in hard rock conditions. The proposed models are compared in terms of accuracy and performance, to select the best models among them. This comparison helps decrease future work expended on choosing appropriate techniques/procedures for analysis of TBM and tunneling data. Thus, the importance of input variables to calculate the TBM PR is estimated and assessed using the selected models. This \"importance\" calculation can enable a better understanding of the predictive capacity of different input parameters. It is extremely helpful to obtain prior knowledge of the TBM performance in rock excavation projects. This allows for planning construction time and for controlling cost, as well as choosing the excavation method. Several studies noted that predicting the penetration rate is a complex and difficult task because of the interaction between the TBM and rock mass. According to [PERSON] [51], TBM penetration rate directly influences the advance rate, which represents the total distance excavated by the machine divided by the total time. While there is a high cost associated with tunneling projects and using the TBM, utilizing prediction methods, especially ML techniques for predicting the penetration rate of TBM can significantly reduce the total time and cost of the tunneling projects. On the other hand, more investigations are needed in the field of TBM performance when there are different weathering zones in the rock-mass. As stated in several studies [6; 52; 53; 54; 55; 56; 57], the degree of rock-mass weathering has an enormous impact on rock mass properties as well as TBM performance. Hence, weathering index is used as an important input parameter in this study to predict TBM PR. In the following sections, the background of the applied ML models is described, and then, after developments of ML techniques, the best model among them will be selected for PR prediction.