## 3 Results
### Surrounding Rock Excavatability Classification Based on Rock-Breaking Data
#### 3.1.1 Selection of Surrounding Rock Excavatability Evaluation Indexes  
According to previous studies on _FPI_, the specific penetration (\(SP\)) and specific excavation rate (\(SER\)) are the available indicators for evaluating SRE in TBM tunneling [23; 40; 41]:  
\[FPI= \frac{F_{n}}{P} \tag{11}\]  
\[SP= \frac{1}{FPI}=\frac{P}{F_{n}} \tag{12}\]  
Figure 11: Cascade forest structure. Figure 12: Forest decision process (three classifications). \[SER=A\ \times\ SP=A\ \times\frac{P}{F_{n}} \tag{13}\]  
where \(F_{n}\) is the average thrust of a single cutter and is calculated as the ratio of the total thrust to the total number of rolling disc cutters, \(P\) is the penetration, and \(A\) is the cross-sectional area of the tunnel. These three indicators can all be used to evaluate SRE. It should be noted that \(SER\) is the ratio of the excavation volume per revolution to the single cutter thrust. Therefore, \(SER\) was selected as the SRE evaluation index in this study. During the construction of KS Tunnel, according to the mechanical indexes of the surrounding rock, the rock mass types can be categorized into five classes in the order from hard rock to soft rock: II, IIIa, IIIb, IV, and V. Based on the data in the stable phase of TBM operation in KS Tunnel, the correlation between \(SER\) and thrust under different surrounding rock types was analyzed (Figure 14). Figure 11 shows that the \(SER\) of Class IV and V surrounding rock fluctuated within a wide range. Therefore, errors may occur  
Figure 13: Surrounding rock excavatability classification process. when determining rock mass excavability only by using \(SER\). It is worth noting that the penetration rate (\(PR\)) of TBMs shows the surrounding rock's information in terms of penetration efficiency and can be used as an important indicator of rock resistance to rock-breaking cutter and rolling disc cutter wear [15]. Figures 15 and 16 also show that PR showed coefficient of determination values (R\({}^{2}\)) of 0.8098 and 0.8248 with respect to \(R_{c}\) and \(K_{\omega}\), respectively, indicating that \(PR\) correlated well with the characteristics of the surrounding rock. Therefore, in this study, \(PR\) was used as another SRE evaluation index to analyze the actual relationships between TBM parameters and geological conditions. \(PR\) is expressed as  
\[PR=\frac{60 RPM\ \times\ P}{1000} \tag{14}\]  
where \(RPM\) is the cutterhead rotational speed (revolutions per minute). 1.2 Construction of a Surrounding Rock Excavatability Classification System Based on the \(Bq\) Index  
\(SER\) and \(PR\) were used to establish a comprehensive SRE evaluation system in this study. However, the classification basis for \(SER\) and \(PR\) is still lacking. Thus, the \(BQ\) rock mass classification method was used as a reference in this study. The \(BQ\) rock mass classification method is a surrounding rock classification method that is commonly used in China, and it can evaluate surrounding rock stability from three aspects: rock, rock mass structure, and rock mass strength parameters. Factors affecting surrounding rock stability are comprehensively considered [33]. However, in TBM tunnels, in addition to surrounding rock stability, TBM parameters and artificially controlled tunneling parameters significantly affect the rapid tunneling of TBMs. It is also known that \(PR\) is positively correlated with thrust at the same rock mass class. However, under the traditional \(BQ\) rock mass classification method, no clear correlation between \(PR\) and thrust was found in the same rock class [42]. Therefore, the conventional \(BQ\) rock mass classification method still needs to be improved for the SRE classification of TBM tunnels. As a reflection of interactions between TBM disc cutters and surrounding rock, penetration can effectively reflect the geological information under different surrounding rock conditions.