## 4 Conclusions  
This study analyzed the correlation between the brittleness of rock and cutting performance parameters with the LCM and CAI test results performed on Korean rocks. Based on the relationships analyzed, we presented several empirical equations for predicting cutter forces, specific energy, and CAI, which are important design parameters of TBM. Further, it was concluded that these parameters can be approximately estimated from the mechanical properties and rock brittleness. The main results and findings of this paper are as follows:  
* The normal force of a disc cutter was correlated with the UCS and BTS of rocks. It also had a good linear relationship with the brittleness indices, especially with B\({}_{1}\).
* Among the four brittleness indices, B\({}_{3}\) and B\({}_{4}\) were correlated with the optimum specific energy; additionally, it was found that the optimum specific energy was highly correlated with the UCS of rocks. In addition, the specific energy had positive relationships with the penetration depth, UCS, BTS, and brittleness indices, while it had negative relationships with the cutter spacing.
* The optimum s/p ratio was found to range from 7.5 to 18 in the current LCM database, and it had negative linear relationships with brittleness and the UCS of rocks.
* It was found that the CAI was slightly correlated with the brittleness indices, and that it was highly correlated with the EQC. A significant linear relationship between the CAI and EQC was found. Furthermore, the CAI prediction models featuring the EQC showed high predictability.  
The main results and findings of this study should serve as useful references for determining the excavation rates of machines, the construction period, and the cutting tool consumption through using several rock properties in the preliminary design stage of TBM tunneling. In order to improve the reliability of the results, the research database should be expanded continuously.  
Conceptualization, H.J.; methodology, H.J. and S.C.; formal analysis, H.J. and S.C.; writing--original draft preparation, H.J.; writing--review and editing, Y.-K.L.; visualization, H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIT) (No. NRF-2021R1G1A1091572).  
Not applicable.  
Data Availability Statement: Data available on request.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Parameters** & **Equations** & \\ \hline B\({}_{1}\), EQC & CAI = 0.46 \(\times\) B\({}_{1}\)\({}^{0.036}\)\(\times\) EQC\({}^{0.41}\) & (R\({}^{2}\) = 0.59) \\ B\({}_{2}\), EQC & CAI = 0.52 \(\times\) B\({}_{2}\)\({}^{0.173}\)\(\times\) EQC\({}^{0.415}\) & (R\({}^{2}\) = 0.59) \\ B\({}_{3}\), EQC & CAI = 0.36 \(\times\) B\({}_{3}\)\({}^{0.066}\)\(\times\) EQC\({}^{0.427}\) & (R\({}^{2}\) = 0.61) \\ B\({}_{4}\), EQC & CAI = 0.41 \(\times\) B\({}_{4}\)\({}^{0.024}\)\(\times\) EQC\({}^{0.426}\) & (R\({}^{2}\) = 0.60) \\ UCS, EQC & CAI = 0.35 \(\times\) UCS\({}^{0.067}\)\(\times\) EQC\({}^{0.427}\) & (R\({}^{2}\) = 0.61) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction models for the Cerchar abrasiveness index.