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Open AccessArticle
Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
by
Hyun-Koo Lee
Hyun-Koo Lee 1,
Myung-Kyu Song
Myung-Kyu Song 1,
Young-Oh Jeong
Young-Oh Jeong 2 and
Sean Seung-Won Lee
Sean Seung-Won Lee 1,*
1
Department of Earth Resources and Environmental Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
2
Partprime Inc., Room no. 714, 193, Gwangdukdae-ro, Gyeonggi-do, Ansan-si 15461, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 9024; https://doi.org/10.3390/app14199024 (registering DOI)
Submission received: 26 August 2024
/
Revised: 29 September 2024
/
Accepted: 30 September 2024
/
Published: 6 October 2024
Abstract
To mitigate unforeseen incidents, such as key block failure or tunnel collapse during excavation, an appropriate support pattern that correlates with the geological conditions of the rock mass at the tunnel face should be designed. Rock mass evaluations should be conducted through geological face mapping during the construction phase, alongside predictions based on field investigations during the design phase. When marked discrepancies are identified, it is customary to convene an on-site evaluation involving a committee of experts. This study develops a digital tunnel face mapping system that utilises mobile devices to facilitate online evaluations during the construction phase. This system effectively replaces the traditional on-site field evaluation method. Tunnel face mapping can be promptly accomplished using images captured at the excavation face, enabling rapid analysis. In conjunction with the mapping capabilities, the developed system was designed to digitally store geological information, which includes parameters such as rock strength distribution, the spacing and length of discontinuities observed during the mapping process, as well as data pertaining to weathering and the groundwater conditions of those discontinuities. This information was then correlated with the rock mass rating sheet to automate the determination of ratings for each parameter, ultimately leading to a conclusive classification of the rock mass quality. By employing this system for tunnel face mapping and rock quality evaluation, we significantly reduced the discrepancies in the evaluation results that often arise due to the subjective judgement of geologists, as well as human errors that can occur throughout the rating process.
Share and Cite
MDPI and ACS Style
Lee, H.-K.; Song, M.-K.; Jeong, Y.-O.; Lee, S.S.-W.
Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping. Appl. Sci. 2024, 14, 9024.
https://doi.org/10.3390/app14199024
AMA Style
Lee H-K, Song M-K, Jeong Y-O, Lee SS-W.
Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping. Applied Sciences. 2024; 14(19):9024.
https://doi.org/10.3390/app14199024
Chicago/Turabian Style
Lee, Hyun-Koo, Myung-Kyu Song, Young-Oh Jeong, and Sean Seung-Won Lee.
2024. "Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping" Applied Sciences 14, no. 19: 9024.
https://doi.org/10.3390/app14199024
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