# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 5 Conclusions  
Conventional manual tunnel face mapping relies heavily on the geologist's direct visual observation, which often presents challenges in accurately assessing tunnel conditions. Factors such as inadequate lighting and ventilation, along with the limited time available for investigation, particularly when subsequent construction activities are imminent, can hinder the evaluation. Therefore, this approach may not consistently yield reliable results. In contrast, adopting digital mapping techniques allows for the excavation face to be documented using mobile devices, such as tablets. Geologists can subsequently enhance captured images by adding geological information related to rock quality, joint conditions, and groundwater presence after relocating to a safe distance from the construction site. This methodology not only facilitates the rapid and accurate documentation of geological information but also minimises interference with ongoing construction processes. Furthermore, in regions where real-time communication is unavailable, mapping and rating can be executed using mobile applications, with data temporarily stored on the mobile device. Upon regaining connectivity, a hybrid system can be employed to automatically upload data to a centralised server. During the digital mapping of the tunnel excavation face, critical information regarding rock strength, geometric characteristics of joints, and groundwater inflow is stored as digital data within the database of the system. The software subsequently aligns these data with the RMR classification table to ascertain the quality grade based on predefined parameters to calculate the corresponding rating value. Consequently, by capturing images and performing digital mapping, the RMR sheet is automatically generated, resulting in the efficient production of report files. This enhances the convenience of data storage, preservation, and reporting. The use of high-precision equipment, such as laser scanners, is becoming commonplace for the automatic extraction of joints from the tunnel excavation face and for assessing joint directionality and roughness with improved accuracy. Concurrently, efforts are underway to develop three-dimensional models of discontinuities for tunnel stability analysis, including key block analysis. Artificial intelligence methodologies, especially machine learning, are being explored for automating joint extraction and evaluating rock quality. However, it is important to note that while deep learning can generate geometric information regarding joints from image or scanner data, it struggles to accurately interpret physical properties, such as rock strength, joint filling states, and groundwater conditions. Therefore, there is an imperative need to develop integrative solutions that combine qualitative rock quality insights from direct geologist surveys with digital information to enhance the overall rock quality evaluation process. Beyond the digital mapping of tunnel faces, further investigation into three-dimensional geological mapping is necessary. This includes predicting fault zone developments, analysing tunnel stability, and implementing automatic rock quality evaluations that lever  
Figure 14: A diagram of an online rock mass rating system using digital mapping. age images integrated with artificial intelligence, virtual reality, and augmented reality technologies. Such advancements can greatly enhance the accuracy and efficiency of geological assessments in tunnel engineering. Digital mapping tool development, H.-K.L. ; coding, Y.-O.J. ; coding assistant, M.-K.S. ; investigation, H.-K.L. ; methodology, S.S.-W.L. ; validation, M.-K.S. ; writing--original draft preparation, H.-K.L. ; conceptualisation, M.-K.S. ; supervision, S.S.-W.L. ; writing--review and editing, S.S.-W.L. All authors have read and agreed to the published version of the manuscript.