Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
권호 표지
DOI QR코드

DOI QR Code

Technical Development for Extraction of Discontinuities in Rock Mass Using LiDAR

LiDAR를 이용한 암반 불연속면 추출 기술의 개발 현황

  • Lee, Hyeon-woo (Dept. of Integrated Energy and Infra system, Kangwon National University) ;
  • Kim, Byung-ryeol (Korea Institute of Limestone & Advanced Materials) ;
  • Choi, Sung-oong (Dept. of Energy and Resources Engineering/Integrated Energy and Infra system, Kangwon National University)
  • 이현우 (강원대학교 신산업개발T-EMS융합전공) ;
  • 김병렬 (한국석회석신소재연구소) ;
  • 최성웅 (강원대학교 에너지.자원공학전공 및 신산업개발T-EMS융합전공)
  • Received : 2021.02.17
  • Accepted : 2021.02.23
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Rock mass classification for construction of underground facilities is essential to secure their stabilities. Therefore, the reliable values for rock mass classification from the precise information on rock discontinuities are most important factors, because rock mass discontinuities can affect exclusively on the physical and mechanical properties of rock mass. The conventional classification operation for rock mass has been usually performed by hand mapping. However, there have been many issues for its precision and reliability; for instance, in large-scale survey area for regional geological survey, or rock mass classification operation by non-professional engineers. For these reasons, automated rock mass classification using LiDAR becomes popular for obtaining the quick and precise information. But there are several suggested algorithms for analyzing the rock mass discontinuities from point cloud data by LiDAR scanning, and it is known that the different algorithm gives usually different solution. Also, it is not simple to obtain the exact same value to hand mapping. In this paper, several discontinuity extract algorithms have been explained, and their processes for extracting rock mass discontinuities have been simulated for real rock bench. The application process for several algorithms is anticipated to be a good reference for future researches on extracting rock mass discontinuities from digital point cloud data by laser scanner, such as LiDAR.

지하 구조물 구축 시 구조물의 안정성을 확보하기 위해서는 주변 암반에 대한 암반 분류가 필수적으로 수행해야 한다. 특히 암반 내에 존재하는 불연속면은 암반의 물리적, 역학적 특성에 지배적인 영향을 미치므로 암반 불연속면에 대한 정확한 정보의 획득을 통해 신뢰도 높은 암반분류값을 제시하는 것은 매우 중요한 요소이다. 이러한 암반 분류는 지금까지 대부분 수작업을 통해 수행되었다. 그러나 대규모 지질조사와 같은 대형 조사면적에 대한 정확도의 부재, 비숙련자에 의한 암반 등급 결정값의 신뢰도 결여 등에 대한 문제점들이 항시 제기되어 왔다. 따라서 최근에 와서는 넓은 범위에 대해서도 신속하고 정확한 암반 분류를 위해 LiDAR를 이용한 암반 분류의 자동화에 대한 연구가 국내·외적으로 널리 이루어지고 있는 추세이다. 그러나 LiDAR 촬영으로 획득되는 point cloud로부터 불연속면의 정보를 분석하는 알고리즘의 특성에 따라 상이한 결과가 도출될 수 있으며, 숙련자에 의한 수작업의 결과를 완벽하게 대체하기에는 미흡한 경우가 종종 발생하고 있다. 따라서 본 연구에서는 LiDAR 촬영으로 획득한 point cloud로부터 불연속면을 추출하는 다양한 알고리즘을 설명하였으며, 이들 알고리즘을 이용하여 실제 암반 사면을 대상으로 불연속면을 추출하는 과정을 분석하였다. 본 연구에서 제시하는 다양한 알고리즘의 적용 과정은 향후 LiDAR 등을 통하여 획득한 디지털 데이터로부터 암반 불연속면을 추출하는 연구에서 참고자료로 활용될 것을 기대한다.

Keywords

Acknowledgement

본 연구는 2020년도 정부(과학기술정보통신부, 환경부, 산업통상자원부)의 재원으로 한국연구재단-탄소자원화 국가전략프로젝트사업(NRF-2017M3D8A2085342)과 이공분야기초연구사업(NRF-2019R1F1A1062714)의 지원으로 수행되었습니다.

References

  1. Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., and Taubin, G., 1999, The ball-pivoting algorithm for surface reconstruction, IEEE transactions on visualization and computer graphics, 5, 4 349-359. https://doi.org/10.1109/2945.817351
  2. Cai, M., Kaiser, P. K., Uno, H., Tasaka, Y., and Minami, M., 2004, Estimation of rock mass deformation modulus and strength of jointed hard rock masses using the GSI system, International Journal of Rock Mechanics and Mining Sciences, 41, 1, 3-19. https://doi.org/10.1016/S1365-1609(03)00025-X
  3. Choe, H. Y., 2017, LiDAR sensor technology and trends, World of Electricity , 66, 9, 12-17.
  4. Delaunay B., 1934, Sur la sphere vide. A la memoire de Georges Voronoi, Bulletin de l'Academie des Sciences de l'URSS. Classe des sciences mathematiques et na, No. 6, 793-800.
  5. Fernandez, O., 2005, Obtaining a best fitting plane through 3D georeferenced data, Journal of Structural Geology, 27, 5, 855-858. https://doi.org/10.1016/j.jsg.2004.12.004
  6. Fischler, M. A. and Bolles, R. C., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24, 6, 381-395. https://doi.org/10.1145/358669.358692
  7. Garcia-Cortes, S., Galan, C. O., Arguelles-Fraga, R., and Diaz, A. M., 2012, Automatic detection of discontinuities from 3D point clouds for the stability analysis of jointed rock masses, 18th International Conference on Virtual Systems and Multimedia, 595-598.
  8. Han, X., Yang, S., Zhou, F., Wang, J., and Zhou, D., 2017, An effective approach for rock mass discontinuity extraction based on terrestrial LiDAR scanning 3D point clouds, Ieee Access, 5, 26734-26742. https://doi.org/10.1109/ACCESS.2017.2771201
  9. Kemeny, J., Turner, K., and Norton, B., 2006, LIDAR for rock mass characterization: hardware, software, accuracy and best-practices, In Workshop on Laser and Photogrammetric Methods for Rock Mass Characterization: Exploring New Opportunities, Golden, Colorado, USA.
  10. Kim, C. H. and Kemeny, J. 2009, Automatic extraction of fractures and their characteristics in rock masses by LIDAR system and the Split-FX software, Tunnel and Underground Space, 19, 1, 1-10.
  11. Kong, D., Wu, F., and Saroglou, C. 2020, Automatic identification and characterization of discontinuities in rock masses from 3D point clouds, Engineering Geology, 265, 105442. https://doi.org/10.1016/j.enggeo.2019.105442
  12. Lee, S. and Jeon, S., 2016, A Study on the Extraction of Slope Surface Orientation using LIDAR with respect to Triangulation Method and Sampling on the Point Cloud, Tunnel and Underground Space, 26, 1, 46-58. https://doi.org/10.7474/TUS.2016.26.1.046
  13. Lee, S., 2020, Automated Characterization of Rock Mass Discontinuities Using LiDAR Point Cloud, Ph.D thesis, Seoul National University.
  14. Li, X., Chen, Z., Chen, J., and Zhu, H., 2019, Automatic characterization of rock mass discontinuities using 3D point clouds, Engineering Geology, 259, 105-131.
  15. Oh, S., 2011, Extraction of Rock Discontinuity Orientation by Laser Scanning Technique, Master's thesis ,Seoul National University.
  16. Poppinga, J., Vaskevicius, N., Birk, A., and Pathak, K., 2008, Fast plane detection and polygonalization in noisy 3D range images, IEEE/RSJ International Conference on Intelligent Robots and Systems, 3378-3383.
  17. Riquelme, A. J., Abellan, A., Tomas, R., and Jaboyedoff, M., 2014, A new approach for semi-automatic rock mass joints recognition from 3D point clouds, Computers & Geosciences, 68, 38-52. https://doi.org/10.1016/j.cageo.2014.03.014
  18. Roncella, R. and Forlani, G., 2005, Extraction of planar patches from point clouds to retrieve dip and dip direction of rock discontinuities, Proceedings of the ISPRS Workshop Laser scanning 2005, ISPRS Archives, Vol. 36, 162-167.
  19. Weitkamp, C., 2006, Lidar: range-resolved optical remote sensing of the atmosphere, Vol. 102, Springer Science & Business.