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Identification of Persistent Discontinuities on a Granitic Rock Mass Through 3D Datasets and Traditional Fieldwork: A Comparative Analysis

  • Adrián RiquelmeEmail author
  • Nuno Araújo
  • Miguel Cano
  • José Luis Pastor
  • Roberto Tomás
  • Tiago Miranda
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)

Abstract

Geologists and engineers traditionally characterise rock slopes through laboratory tests and data captured during fieldwork and further cabinet work. They find, however, difficulties in capturing data in terms of safety, objectiveness and reliability. However, the continuous improvement of remote sensing techniques is changing the rock slope stability analysis. Light Detection and Ranging (LiDAR) and Structure from Motion (SfM) derived datasets comprise 3D point clouds that represent the studied ground surface. This data permits the geometric analysis and the extraction of the number of discontinuity sets affecting the rock mass, and their orientation, normal spacing and persistence. Despite the importance of persistence for characterising discontinuities, the user must previously decide on the field whether a discontinuity is persistent or non-persistent. Contrarily, the use of 3D datasets enables the establishment of objective criteria during the characterisation of rock masses. In this work, we present a comparative analysis of the persistence of a rock slope in Braga (Portugal). An experienced engineer analysed the discontinuities. Besides, the rock slope was digitised through the SfM technique, enabling the analysis and extraction of their discontinuity sets. The results showed that despite the aid of the 3D point clouds, the fieldwork still plays a key role in the field recognition and discontinuities characterisation. However, the use of 3D point clouds provides objective information to enhance the analysis of a rock slope.

Keywords

Discontinuity Persistence Remote sensing SfM 

References

  1. 1.
    Zhang, L.: Rock discontinuities. In: Zhang, L. (ed.) Engineering Properties of Rocks, pp. 53–97. Elsevier (2006)Google Scholar
  2. 2.
    Abellán, A., Derron, M.-H., Jaboyedoff, M.: “Use of 3D point clouds in geohazards” special issue: current challenges and future trends. Remote Sens. 8, 130 (2016).  https://doi.org/10.3390/rs8020130CrossRefGoogle Scholar
  3. 3.
    Riquelme, A., Cano, M., Tomás, R., Abellán, A.: Identification of rock slope discontinuity sets from laser scanner and photogrammetric point clouds: a comparative analysis. Procedia Eng. 191, 838–845 (2017)CrossRefGoogle Scholar
  4. 4.
    Buckley, S.J., Kurz, T.H., Howell, J.A., Schneider, D.: Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis. Comput. Geosci. 54, 249–258 (2013).  https://doi.org/10.1016/j.cageo.2013.01.018CrossRefGoogle Scholar
  5. 5.
    Kurz, T.H., Buckley, S.J., Howell, J.A., Schneider, D.: Integration of panoramic hyperspectral imaging with terrestrial lidar data. Photogramm. Rec. 26, 212–228 (2011).  https://doi.org/10.1111/j.1477-9730.2011.00632.xCrossRefGoogle Scholar
  6. 6.
    Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakkola, A., Guan, F., Holopainen, M., Vastaranta, M.: Terrestrial laser scanning in forest inventories. ISPRS J. Photogramm. Remote Sens. 115, 63–77 (2016).  https://doi.org/10.1016/j.isprsjprs.2016.01.006CrossRefGoogle Scholar
  7. 7.
    Penasa, L., Franceschi, M., Preto, N., Teza, G., Polito, V.: Integration of intensity textures and local geometry descriptors from terrestrial laser scanning to map chert in outcrops. ISPRS J. Photogramm. Remote Sens. 93, 88–97 (2014).  https://doi.org/10.1016/j.isprsjprs.2014.04.003CrossRefGoogle Scholar
  8. 8.
    ISRM: International Society for Rock Mechanics Commission on standardization of laboratory and field tests: suggested methods for the quantitative description of discontinuities in rock masses. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 15, 319–368 (1978).  https://doi.org/10.1016/0148-9062(79)91476-1
  9. 9.
    Bieniawski, Z.T.: Engineering Rock Mass Classifications: A Complete Manual for Engineers and Geologists in Mining, Civil, and Petroleum Engineering. Wiley, New York (1989)Google Scholar
  10. 10.
    Assali, P., Grussenmeyer, P., Villemin, T., Pollet, N., Viguier, F.: Solid images for geostructural mapping and key block modeling of rock discontinuities. Comput. Geosci. 89, 21–31 (2016).  https://doi.org/10.1016/j.cageo.2016.01.002CrossRefGoogle Scholar
  11. 11.
    Chen, J., Zhu, H., Li, X.: Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud. Comput. Geosci. 95, 18–31 (2016).  https://doi.org/10.1016/j.cageo.2016.06.015CrossRefGoogle Scholar
  12. 12.
    Chen, N., Kemeny, J., Jiang, Q., Pan, Z.: Automatic extraction of blocks from 3D point clouds of fractured rock. Comput. Geosci. 109, 149–161 (2017).  https://doi.org/10.1016/j.cageo.2017.08.013CrossRefGoogle Scholar
  13. 13.
    Ferrero, A.M., Forlani, G., Roncella, R., Voyat, H.I.: Advanced geostructural survey methods applied to rock mass characterization. Rock Mech. Rock Eng. 42, 631–665 (2009).  https://doi.org/10.1007/s00603-008-0010-4CrossRefGoogle Scholar
  14. 14.
    Gigli, G., Casagli, N.: Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds. Int. J. Rock Mech. Min. Sci. 48, 187–198 (2011).  https://doi.org/10.1016/j.ijrmms.2010.11.009CrossRefGoogle Scholar
  15. 15.
    Gomes, R.K., de Oliveira, L.P.L., Gonzaga, L., Tognoli, F.M.W., Veronez, M.R., de Souza, M.K.: An algorithm for automatic detection and orientation estimation of planar structures in LiDAR-scanned outcrops. Comput. Geosci. 90, 170–178 (2016).  https://doi.org/10.1016/j.cageo.2016.02.011CrossRefGoogle Scholar
  16. 16.
    Jaboyedoff, M., Metzger, R., Oppikofer, T., Couture, R., Derron, M.-H., Locat, J., Turmel, D.: New insight techniques to analyze rock-slope relief using DEM and 3D-imaging cloud points: COLTOP-3D software. In: Francis, T. (ed.) Rock Mechanics: Meeting Society’s Challenges and Demands, Proceedings of the 1st Canada - U.S. Rock Mechanics Symposium, Vancouver, Canada, 27–31 May 2007, pp. 61–68 (2007)CrossRefGoogle Scholar
  17. 17.
    Lato, M.J., Vöge, M.: Automated mapping of rock discontinuities in 3D lidar and photogrammetry models. Int. J. Rock Mech. Min. Sci. 54, 150–158 (2012).  https://doi.org/10.1016/j.ijrmms.2012.06.003CrossRefGoogle Scholar
  18. 18.
    Leng, X., Xiao, J., Wang, Y.: A multi-scale plane-detection method based on the Hough transform and region growing. Photogramm. Rec. 31, 166–192 (2016).  https://doi.org/10.1111/phor.12145CrossRefGoogle Scholar
  19. 19.
    Olariu, M.I., Ferguson, J.F., Aiken, C.L.V., Xu, X.: Outcrop fracture characterization using terrestrial laser scanners: deep-water Jackfork sandstone at big rock quarry, Arkansas. Geosphere 4, 247–259 (2008)CrossRefGoogle Scholar
  20. 20.
    Riquelme, A., Abellán, A., Tomás, R., Jaboyedoff, M.: A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Comput. Geosci. 68, 38–52 (2014).  https://doi.org/10.1016/j.cageo.2014.03.014CrossRefGoogle Scholar
  21. 21.
    Slob, S., van Knapen, B., Hack, H.R.G.K., Turner, K., Kemeny, J.: Method for automated discontinuity analysis of rock slopes with three-dimensional laser scanning. Transp. Res. Rec. 1913, 187–194 (2005).  https://doi.org/10.3141/1913-18CrossRefGoogle Scholar
  22. 22.
    Sturzenegger, M., Stead, D., Elmo, D.: Terrestrial remote sensing-based estimation of mean trace length, trace intensity and block size/shape. Eng. Geol. 119, 96–111 (2011).  https://doi.org/10.1016/j.enggeo.2011.02.005CrossRefGoogle Scholar
  23. 23.
    Sturzenegger, M., Stead, D., Beveridge, A., Lee, S., Van As, A.: Long-range terrestrial digital photogrammetry for discontinuity characterization at Palabora open-pit mine. In: Third Canada–US Rock Mechanics Symposium, Paper, pp. 1–10 (2009)Google Scholar
  24. 24.
    Wang, X., Zou, L., Shen, X., Ren, Y., Qin, Y.: A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud. Comput. Geosci. 99, 100–106 (2017).  https://doi.org/10.1016/j.cageo.2016.11.002CrossRefGoogle Scholar
  25. 25.
    Riquelme, A., Abellán, A., Tomás, R.: Discontinuity spacing analysis in rock masses using 3D point clouds. Eng. Geol. 195, 185–195 (2015).  https://doi.org/10.1016/j.enggeo.2015.06.009CrossRefGoogle Scholar
  26. 26.
    Slob, S., Turner, A.K., Bruining, J., Hack, H.R.G.K.: Automated rock mass characterisation using 3-D terrestrial laser scanning (2010). http://www.narcis.nl/publication/RecordID/oai:tudelft.nl:uuid:c1481b1d-9b33-42e4-885a-53a6677843f6
  27. 27.
    Riquelme, A., Tomás, R., Cano, M., Pastor, J.L., Abellán, A.: Automatic mapping of discontinuity persistence on rock masses using 3D point clouds. Rock Mech. Rock Eng. 51, 3005–3028 (2018).  https://doi.org/10.1007/s00603-018-1519-9CrossRefGoogle Scholar
  28. 28.
    Bahrani, N., Tannant, D.D.: Field-scale assessment of effective dilation angle and peak shear displacement for a footwall slab failure surface. Int. J. Rock Mech. Min. Sci. (2011).  https://doi.org/10.1016/j.ijrmms.2011.02.009CrossRefGoogle Scholar
  29. 29.
    Haneberg, W.: Directional roughness profiles from three-dimensional photogrammetric or laser scanner point clouds. In: Eberhardt, E., Stead, D., Morrison, T. (eds.) Rock Mechanics: Meeting Society’s Challenges and Demands, pp. 101–106. Taylor & Francis, Vancouver (2007)CrossRefGoogle Scholar
  30. 30.
    Khoshelham, K., Altundag, D., Ngan-Tillard, D., Menenti, M.: Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning. Int. J. Rock Mech. Min. Sci. 48, 1215–1223 (2011).  https://doi.org/10.1016/j.ijrmms.2011.09.007CrossRefGoogle Scholar
  31. 31.
    Lai, P., Samson, C., Bose, P.: Surface roughness of rock faces through the curvature of triangulated meshes. Comput. Geosci. 70, 229–237 (2014).  https://doi.org/10.1016/j.cageo.2014.05.010CrossRefGoogle Scholar
  32. 32.
    Oppikofer, T., Jaboyedoff, M., Blikra, L., Derron, M.-H., Metzger, R.: Characterization and monitoring of the Åknes rockslide using terrestrial laser scanning. Nat. Hazards Earth Syst. Sci. 9, 1003–1019 (2009).  https://doi.org/10.5194/nhess-9-1003-2009CrossRefGoogle Scholar
  33. 33.
    Hencher, S.R., Lee, S.G., Carter, T.G., Richards, L.R.: Sheeting joints: characterisation, shear strength and engineering. Rock Mech. Rock Eng. 44, 1–22 (2011).  https://doi.org/10.1007/s00603-010-0100-yCrossRefGoogle Scholar
  34. 34.
    Agisoft LLC: Agisoft Metashape Professional (2019). www.agisoft.ru
  35. 35.
    Jordá Bordehore, L., Riquelme, A., Cano, M., Tomás, R.: Comparing manual and remote sensing field discontinuity collection used in kinematic stability assessment of failed rock slopes. Int. J. Rock Mech. Min. Sci. 97, 24–32 (2017).  https://doi.org/10.1016/j.ijrmms.2017.06.004CrossRefGoogle Scholar
  36. 36.
    Riquelme, A., Abellán, A., Tomás, R., Jaboyedoff, M.: Discontinuity Set Extractor (2014). http://rua.ua.es/dspace/handle/10045/50025

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of AlicanteSan Vicente del RaspeigSpain
  2. 2.ISISE, Department of Civil EngineeringUniversity of MinhoGuimarãesPortugal

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