# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 2 Literature Review and Case Study  
### Traditional Rock Mass Classification  
[PERSON] [9] originally introduced the well-established RMR classification system, which is critical in the assessment of rock mass quality. The RMR value is computed as the sum of five critical parameters: rock strength, rock quality designation (RQD), joint spacing, the condition of joint surfaces, and groundwater outflow. Subsequently, the initial RMR value is adjusted based on a correction factor that accounts for the orientation and dip angle of the joints in relation to the tunnel axis, culminating in the final RMR value. Figure 3 illustrates the methodology employed in selecting appropriate rock support types, with the RMR chart as a reference for informed decision making in tunnel engineering. [PERSON] [10] proposed the Q-System, a well-regarded rock mass classification framework that comprises six critical parameters. The Q-value is mathematically defined as  
\[\mathrm{Q}=(\mathrm{RQD}/\mathrm{J}_{\mathrm{n}})\cdot(\mathrm{J}_{\mathrm{r} }/\mathrm{J}_{\mathrm{a}})\cdot(\mathrm{J}_{\mathrm{w}}/\mathrm{SRF}) \tag{1}\]  
where RQD represents the rock quality designation, \(\mathrm{J}_{\mathrm{n}}\) denotes the number of joint sets, \(\mathrm{J}_{\mathrm{r}}\) refers to the roughness of the principal joint set, \(\mathrm{J}_{\mathrm{a}}\) signifies the alteration of the primary joint set, \(\mathrm{J}_{\mathrm{w}}\) is indicative of the groundwater outflow condition, and SRF represents the stress reduction factor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Method** & **Researcher** & \multicolumn{2}{c}{**Key Contents**} \\ \hline Rock load & [PERSON] 1946 [2] & - & Evaluation of rock load height for nine rock conditions \\  & & - & Selection of a rough support pattern for each rock load \\ \hline RQD (rock quality designation) & Deere 1988 [3] & RQD = core length greater than 10 cm/total core length \\  & & five levels of rock quality (very poor–excellent) \\ \hline RSR (rock structure rating) & [PERSON] 1972 [4] & - & Rock type and property, discontinuity properties \\  & & - & Construction parameters: tunnel size, direction of \\  & & drive, method of excavation \\ \hline RMR & [PERSON] 1973 [5] & Five parameters: rock strength, RQD, joint spacing, joint condition, groundwater inflow rate \\  & & Rating score: 0–100 (very poor–very favourable) \\ \hline Q-System & [PERSON] 1974 [6] & Six parameters: RQD, No. of joints set, joint roughness, joint aperture, groundwater, insitu stress \\  & & Rating score: 0–100 (very poor–very favourable) \\ \hline SMR (slope mass rating) & [PERSON] 2015 [7] & Basic RMR x three coefficients by joint orientation (F1–F3) + F4 (coefficient by excavation method) \\ \hline GSI (Geological Strength Index) & [PERSON] 2019 [8] & Detailed classification of very poor rock masses with an \\  & & RMR value of 23 or below \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical rock mass classification methods. The RMR classification method assigns a numerical score between 0 and 100, informed by five characteristics of rock mass quality. The survey and evaluation techniques associated with the RMR are relatively straightforward, contributing to their widespread application in tunnel and slope design, particularly in Korea. In contrast, the rock mass classification method employing the Q-System evaluates rock quality by multiplying values related to the distribution characteristics of joints and the state of filling and roughness of joint surfaces, along with aspects regarding groundwater conditions and local rock mass stress. The complexity of the investigation and assessment of joint characteristics renders it a valuable reference material in domestic tunnel design and promotes its active utilisation in tunnel and slope engineering across Europe and the Middle East. In the digital mapping system developed as part of this study, configurations were established to facilitate rock quality evaluations and report generation by utilising both the RMR and Q-System.