# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 2 Literature Review and Case Study
### New Technologies Related to Digital Mapping of the Tunnel Face  
Table 3 presents a concise summary of emerging technologies related to digital mapping and rock mass characterisation. These advanced techniques represent significant improvements over conventional methods, with advantages in enhanced accuracy, efficiency, and safety in geological assessments.  
The geological conditions in Japan often present significant challenges for tunnel excavation. Consequently, the development and implementation of digital tunnel mapping techniques involving the optical analysis of tunnel faces have become essential tools in the field. These digital mapping techniques are complemented by stability management practices that leverage the data obtained from such analyses to enhance excavation safety and efficacy.  
In Europe, particularly in Austria, extensive research is being conducted on the automatic extraction of discontinuities with the aid of photographic imagery and laser-scanning technologies. This research encompasses the creation of three-dimensional (3D) image models, geological mapping, and tunnel stability analysis, all of which are integral to conventional tunnelling techniques. The advancing methodologies aim to optimise tunnel excavation processes and improve the overall safety and reliability of tunnel infrastructures.  
In South Korea, research and development efforts related to digital tunnel mapping commenced in 2017 and have since been integrated into various tunnelling projects. Current  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **RMR\({}_{\textbf{b}}\) (1989)** & **GSI** & **Remark** \\ \hline Uniaxial & 15 & 0 & \\ Strength & 15 & 0 & \\ \hline Mechanical & & & \\ Structure & 40 & 50 &
\begin{tabular}{c} RQD \\ Joint space \\ \end{tabular} \\ \hline Discontinuities & & & \\ Condition & 30 & 50 & \\ \hline Groundwater & & & \\ Condition & 15 & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rock mass classification by RMR and GSI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** &
\begin{tabular}{c} **Country** \\ **(Company)** \\ \end{tabular} & **Main Function** \\ \hline Four-dimensional super NATM & Japan & A tunnel displacement prediction technique \\ measurement system & (Toda corp.) & using a 3D laser scanner. \\ \hline Shape-Matrix & Austria & Three-dimensional tunnel modelling and \\
3D program & (3 GSM GmbH) [14] & geological digital tunnel mapping using several \\  & & types of image data and a laser scanner. \\ \hline Tunnel program & Greece & Tunnel projection mapping (tunnel face \\ by 3D laser & (Seraphim & mapping using a laser scanner). \\  & Amvrazis) [15] & Forecasting the progress of excavation. \\ \hline Tunnel digital & Korea & Digital mapping of the tunnel face on \\ mapping system & (KICT) [16] & a tunnel photo image. \\ \hline Extraction of discontinuities & Korea (Seoul & Extracting joint sets using a laser scanner and \\  & University) [17; 18; 19] & deep learning. \\ \hline Rock classification prediction in & Korea & Rock quality classification using a convolutional \\ tunnel excavation using CNN & (Samsung E\&C) [20] & neural network (CNN). \\ \hline Automated extraction fracture trace & China ([PERSON], & Extracting fracture and analysing rock structures \\ maps from tunnel face images & Tongji University) [21] & using a CNN. \\ \hline \hline \end{tabular}
\end{table}
Table 3: New technologies related to digital mapping and rock mass characterisation.  
initiatives are focused on using laser scanners in conjunction with deep-learning algorithms to detect rock joints automatically. These advancements also include the calculation of safety factors for critical rock blocks, thereby enhancing the analysis of tunnel stability and addressing the unique geological challenges faced in the region.