## 3 Data  
### Data Preparation and Analysis  
Accurate rock mass quality classification methods rely on the appropriate selection of rock mass indicators. Existing classification methods suggest that the factors influencing rock mass quality evaluation can be mainly divided into three categories: (1) rock mass strength indicators, (2) rock mass structural features, and (3) other geological factors affecting rock mass quality. To make rock mass classification for underground engineering more rational and scientific, the selected evaluation indicators should comprehensively reflect the true quality of the rock mass. Additionally, the feasibility of obtaining these indicators in practical engineering must be considered. In this study, uniaxial compressive strength (UCS) was used as the strength indicator; the rock mass integrity coefficient (Kv) and rock quality designation (RQD) were used as the structural features; and the groundwater condition (W) was used as the geological factor affecting rock mass quality. A rock mass classification database comprising 130 samples was established through a literature review and selection of rock mass classification case data. This database covers various types of geotechnical engineering projects, including tunnels, underground chambers in power stations, and underground mines [16; 17; 18; 19; 20; 21; 22; 23]. The surrounding rock is classified into five grades: very good (class I), good (class II), fair (class III), poor (class IV), and very poor (class V). It is noteworthy that there are no class I cases in the dataset used for this study. However, this does not affect the study's engineering applicability, as very high-quality rock masses are rare in underground or deep rock engineering. Ultimately, a database was created with four input parameters (UCS, RQD, Kv, and W) and one output parameter (rock mass classification). Table 1 provides the statistical descriptions of the four rock mass classifications. As shown in Table 1, as the rock mass classification increases, the values of UCS and RQD decrease, indicating a negative correlation between UCS, RQD, and rock mass classification. The numbers of cases for each rock mass classification in the database are: class II with 37 cases (28.5%), class III with 43 cases (33.1%), class IV with 27 cases (20.8%), and class V with 23 cases (17.7%), indicating an imbalance in the database. Additionally, the correlation matrix of the rock mass classification data in Figure 2 can be used to analyze the correlation and distribution relationships between the data. It is evident that UCS and RQD are strongly correlated with rock mass classification. The scatter plot distribution shows that the range of input parameters is broad, effectively covering the rock mass quality conditions in underground engineering, further demonstrating the reliability and comprehensiveness of the database. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Statistical Indicators** & **UCS** & **RQD** & **Kv** & **W** \\  & & **(MPa)** & **(\%)** & **L (min-10 m)\({}^{-1}\)** \\ \hline  & Count and percentage & 37 (28.5\%) & 37 (28.5\%) & 37 (28.5\%) & 37 (28.5\%) \\  & Mean value & 102.32 & 77.72 & 0.62 & 5.02 \\  & Standard deviation & 25.74 & 6.94 & 0.11 & 5.30 \\  & Min value & 70.00 & 58.13 & 0.30 & 0 \\ class II & 25 th percentile & 90.00 & 75.00 & 0.55 & 0 \\  & 50 th percentile & 94.00 & 77.50 & 0.65 & 1.00 \\  & 75 th percentile & 95.00 & 82.00 & 0.70 & 10.00 \\  & Max value & 181.73 & 90.10 & 0.75 & 17.00 \\ \hline  & Count and percentage & 43 (33.1\%) & 43 (33.1\%) & 43 (33.1\%) & 43 (33.1\%) \\  & Mean value & 66.31 & 55.59 & 0.44 & 21.64 \\  & Standard deviation & 28.90 & 15.49 & 0.16 & 36.34 \\  & Min value & 25.00 & 26.00 & 0.12 & 0 \\ class III & 25 th percentile & 40.00 & 50.00 & 0.32 & 9.50 \\  & 50 th percentile & 70.00 & 52.00 & 0.40 & 15.00 \\  & 75 th percentile & 93.50 & 69.00 & 0.57 & 20.00 \\  & Max value & 127.92 & 88.60 & 0.70 & 223.00 \\ \hline \hline \end{tabular}
\end{table}