# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
## 2 Materials and Methods
### Sample Collection  
The sample of this study was obtained from some tunnels of the Zhengzhou-Wanzhou line of the high-speed railway project in China. This railway line runs from Zhengzhou East Railway Station to Wanzhou North Railway Station, with a total length of 818 km. It has 18 stations and a designed speed of 350 km/h, which connects Henan province, Hubei province, and Chongqing province. There are 32.5 tunnels in the Hubei province section, with a total length of 167.6 km, and the lithologies of stratum exposed by these tunnels are mainly dolomite, sandstone, limestone, shale, and mudstone. As shown in Figure 2, the  
Figure 1: The research flow chart about main procedures and proposed methods. sample collection was carried out in five tunnels in the Hubei province section. The New Austrian Tunneling Method was employed in these tunnels. The sample of intelligent surrounding rock classification is composed of the drilling parameters and the surrounding rock grade of the tunnel face. In this study, the drilling parameters were collected by using the intelligent drill jumbo (Figure 3), which was made by China Railway Construction Heavy Industry Corporation Limited. The intelligent drill jumbo has the functions of automatic positioning, automatic drilling and automatic recording of log. It can complete the drilling operation of pipe roof, anchor bolt and blast hole. In the process, the integrated sensors of the intelligent drill jumbo are used to automatically collect the drilling parameters (penetration velocity, hammer pressure, rotation pressure, and feed pressure) during the drilling of the borehole in the tunnel face. The drilling parameters are described below:  
1. Penetration velocity (\(V_{p}\), m/min): the rate of penetration of the drill bit through the rock mass. Figure 3: Intelligent drill jumbo (made by China Railway Construction Heavy Industry Corporation Limited). Figure 2: Locations of Zhengzhou–Wanzhou high-speed railway line and the tunnels where the samples were collected. 2. Hammer pressure (\(P_{h}\), bar): the measurement of the impact pressure of the bit against the rock mass. 3. Rotation pressure (\(P_{r}\), bar): the pressure of the bit against the rock to maintain the required rotation. 4. Feed pressure (\(P_{f}\), bar): the hydraulic pressure inside the cylinders required to keep the bit in contact with the bottom of the hole. All of these parameters are recorded at equal depth intervals of 20 mm by the help of displacement transducer. The drilling parameters data in this study were collected through the process of blasting hole drilling in the tunnel face (Figure 4a), and the bit used was a cemented carbide bit with a diamond content of 7%, spherical shape, and nine teeth (Figure 4b). The area of the tunnel face is about 150 m\({}^{2}\), containing about 200-300 blasting holes. The value of each drilling parameter for each sample is the average of all the drilling holes in the tunnel face. The typical layout of the blasting hole is shown in Figure 5.  
The grade of the surrounding rock in this study is specified in the current Code for Design of Railway Tunnel (TB10003-2016). According to the degree of hardness, integrity, groundwater state, crustal stress state, and major weak structural surface, the surrounding rocks are divided into Grades I-VI according to the quality. The approximate correspondence between the surrounding rock grade in this study and the indices of other classification methods, such as the Q method, RMR method, and BQ method, is shown in Table 1[55].