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Bulletin of Engineering Geology and the Environment

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01.04.2023 | Original Paper

Determination of joint roughness coefficient using a cost-effective photogrammetry technique

verfasst von: Sakshi Rohilla, Resmi Sebastian

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 4/2023

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Abstract

The presence of discontinuities in the rock mass is extremely crucial in many engineering applications of rock mechanics. The estimation of the joint roughness coefficient (JRC) utilising easily accessible camera-friendly devices and application software has recently gained prominence. The joint roughness coefficient of a jointed rough surface may be calculated using either contact methods (physically contacting the surface to get the profile) or non-contact methods (extraction of the profile without physically touching the surface using laser profilometry or 3D laser scanning and photogrammetry techniques). The 3D laser scanner is widely used for its accuracy in obtaining the surface topography of rock masses; however, the practice is often uneconomical due to the high equipment cost. In this study, the smartphone photogrammetry technology is used to extract the three-dimensional surface topography of a jointed rough surface in order to evaluate the JRC. For the comparative study, the surface profile was retrieved using a surface profilometer, 3D scanner and photogrammetry method. The JRC values for the surface profiles gathered through smartphone photogrammetry, 3D scanning and surface profilometer have been estimated using visual analysis, fractal dimension and statistical techniques. The study found that the surface profile generated using the photogrammetric approach utilising smartphone delivers more realistic profiling than the surface profilometer because of the lower least count and greater precision. The predicted JRC values for smartphone photogrammetry profiles agreed well with the JRC values obtained for 3D scanner and surface profilometer profiles.

Vorheriger Artikel Predicting geomechanical, abrasivity, and drillability properties in some igneous rocks using fabric features and petrographic indexes

Nächster Artikel An experimental study of fault slips under unloading condition in coal mines

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An P, Fang K, Jiang Q, Zhang H, Zhang Y (2021) Measurement of rock joint surfaces by using smartphone structure from motion (SFM) photogrammetry. Sensors 21(3):922. https://doi.org/10.3390/s21030922CrossRef

Andrade PS, Saraiva AA (2008) Estimating the joint roughness coefficient of discontinuities found in metamorphic rocks. Bull Eng Geol Environ 67:425–434. https://doi.org/10.1007/s10064-008-0151-4CrossRef

Babanouri N, Nasab SK, Sarafrazi S (2013) A hybrid particle swarm optimization and multi-layer perceptron algorithm for bivariate fractal analysis of rock fractures roughness. Int J Rock Mech Min Sci 60:66–74. https://doi.org/10.1016/j.ijrmms.2012.12.028CrossRef

Bae D, Kim K, Koh Y, Kim J (2011) Characterization of joint roughness in granite by applying the scan circle technique to images from a borehole televiewer. Rock Mech Rock Eng 44:497–504. https://doi.org/10.1007/s00603-011-0134-9CrossRef

Bandis S, Lumsden AC, Barton N (1981) Experimental studies of scale effects on the shear behaviour of rock joints. Int J Rock Mech Min Sci Geomech Abstr 18:1–21. https://doi.org/10.1016/0148-9062(81)91009-3CrossRef

Barton N (1982) Modelling rock joint behaviour from in situ block tests: implications for nuclear waste repository design. Office of Nuclear Waste Isolation ONWI-308: Columbus, OH; 1982. p. 96, ONWI-308

Barton N, Choubey V (1977) The shear strength of rock joints in theory and practice. Rock Mech 10(1):1–54. https://doi.org/10.1007/bf01261801CrossRef

Belem T, Souley M, Homand F (2007) Modeling surface roughness degradation of rock joint wall during monotonic and cyclic shearing. Acta Geotech 2:227–248. https://doi.org/10.1007/s11440-007-0039-7CrossRef

Bretar F, Arab-Sedze M, Champion J et al (2013) An advanced photogrammetric method to measure surface roughness: application to volcanic terrains in the Piton de la Fournaise, Reunion Island. Remote Sens Environ 135:1–11. https://doi.org/10.1016/j.rse.2013.03.026CrossRef

Brideau MA, Sturzenegger M, Stead D, Jaboyedoff M, Lawrence M, Roberts N, Ward B, Millard T, Clague J (2012) Stability analysis of the 2007 Chehalis Lake landslide based on long-range terrestrial photogrammetry and airborne LiDAR data. Landslides 9:75–91. https://doi.org/10.1007/s10346-011-0286-4CrossRef

Brown M, Lowe DG (2005) Unsupervised 3D object recognition and reconstruction in unordered datasets. Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modelling (3DIM’05), Ottawa, ON, Canada, pp 56–63 (13–16 June 2005)

Carr JR, Warriner JB (1987) Rock mass classification of joint roughness coefficient. Proceedings of the 28th US rock mechanics symposium, Tucson, pp 72–86

Carrivick JL, Smith MW, Quincey DJ (2016) Structure from motion in the geosciences. John Wiley & Sons, Hoboken, NJ, USA, pp 48–51CrossRef

Chen SJ, Zhu WC, Zhang MS, Yu QL (2012) Fractal description of rock joints based on digital image processing technique. Chin J Geotech Eng 34(11):2087–2092

Chen SJ, Zhu WC, Yu QL, Liu XG (2016) Characterization of anisotropy of joint surface roughness and aperture by variogram approach based on digital image processing technique. Rock Mech Rock Eng 49(3):855–876. https://doi.org/10.1007/s00603-015-0795-xCrossRef

Cheng Q (1997) Multi fractal modelling and lacunarity analysis. Math Geol 29(7):919–32CrossRef

CloudCompare Omnia version:2.9.1 Stereo (2014a) Complied with MSVC 1800 and Qt 5.9.1, windows 64-bit. http://www.cloudcompare.org/

Corradetti A, McCaffery KJW, Paola ND, Tavani S (2017) Evaluating roughness scaling properties of natural active fault surfaces by means of multi-view photogrammetry. Tectonophysics PII: S0040–1951(17)30342–6. https://doi.org/10.1016/j.tecto.2017.08.023CrossRef

Dershowitz W, Einstein H (1988) Characterizing rock joint geometry with joint system models. Rock Mech Rock Eng 21(1):21–51. https://doi.org/10.1007/bf01019674CrossRef

Du S (1994) The directive statistical evaluation of rock joint roughness coefficient (JRC). J Eng Geol 2(3):62–71 (in Chinese)

Du S, Xu S, Yang S (2000) Application of rock quality designation (RQD) to engineering classification of rocks. J Eng Geol 8(3):351–356 (in Chinese)

Du S, Hu Y, Hu X (2009) Measurement of joint roughness coefficient by using profilograph and roughness ruler. J Earth Sci 20(5):890–896. https://doi.org/10.1007/s12583-009-0075-3CrossRef

Fan W, Cao P (2019) A new 3D JRC calculation method of rock joint based on laboratory-scale morphology testing and its application in shear strength analysis. Bull Engg Geol Environ. https://doi.org/10.1007/s10064-019-01569-0CrossRef

Fardin N, Stephansson O, Jing L (2001) The scale dependence of rock joint surface roughness. Int J Rock Mech Min Sci 2001(38):659–669. https://doi.org/10.1016/s1365-1609(01)00028-4CrossRef

Fardin N, Feng Q, Stephansson O (2004) Application of a new in situ 3D laser scanner to study the scale effect on the rock joint surface roughness. Int J Rock Mech Min Sci 41(2):329–335. https://doi.org/10.1016/S1365-1609(03)00111-4CrossRef

Ferrero AM, Migliazza M, Roncella R, Rabbi E (2011) Rock slopes risk assessment based on advanced geostructural survey techniques. Landslides 8:221–231. https://doi.org/10.1007/s10346-010-0246-4CrossRef

Firpo G, Salvini R, Francioni M, Ranjith PG (2011) Use of digital terrestrial photogrammetry in rocky slope stability analysis by distinct elements numerical methods. Int J Rock Mech Min Sci 48(7):1045–1054. https://doi.org/10.1016/j.ijrmms.2011.07.007CrossRef

Gao Y, Ngai L, Wong Y (2015) A modified correlation between roughness parameter Z2 and the JRC. Rock Mech Rock Eng 48(1):387–396. https://doi.org/10.1007/s00603-013-0505-5CrossRef

García-Luna R, Senent S, Jimenez R (2021) Using telephoto lens to characterize rock surface roughness in SfM models. Rock Mech Rock Eng 54(5):2369–2382. https://doi.org/10.1007/s00603-021-02373-7CrossRef

Guo H, Karekal S, Poropat G, Soole P, Lambert C (2011) Pit wall strength estimation with 3D imaging. CSIRO, ACARP, Brisbane

Haneberg WC (2007) Directional roughness profiles from three-dimensional photogrammetric or laser scanner point clouds. 1st Canada–US Rock Mechanics Symposium, Vancouver, pp 101–106

Herda HHW (2006) An algorithmic 3D rock roughness measure using local depth measurement clusters. Rock Mech Rock Eng 39(2):147–158. https://doi.org/10.1007/s00603-005-0063-6CrossRef

Hotar V, Novotny F (2005) Surface profile evaluation by fractal dimension and statistic tools. Proceeding of 11th International Congress on Fracture (ICF), Turin, Italy, pp 20–25

Hu XQ, Cruden DM (1992) A portable tilting table for on-site tests of the friction angles of discontinuities in rock masses. Bull Int Assoc Eng Geol 46(1):59–62CrossRef

ISRM (1978) Suggested methods for the quantitative description of discontinuities in rock masses. Int J Rock Mech Min Sci Geomech Abstr 15(6):319–368. https://doi.org/10.1016/0148-9062(78)91472-9CrossRef

Jiang Y, Li B, Tanabashi Y (2006) Estimating the relation between surface roughness and mechanical properties of rock joints. Int J Rock Mech Min Sci 43(6):837–846. https://doi.org/10.1016/j.ijrmms.2005.11.013CrossRef

Kulatilake PHSW, Balasingam P, Park J, Morgan R (2006) Natural rock joint roughness quantification through fractal techniques. Geotech Geol Eng 24(5):1181. https://doi.org/10.1007/s10706-005-1219-6CrossRef

Kwasniewski M, Wang JA (1997) Surface roughness evolution and mechanical behavior of rock joints under shear. Int J Rock Mech Min Sci 34(3–4):709–709. https://doi.org/10.1016/s0148-9062(97)00157-5CrossRef

Lamas LN (1996) An experimental and analytical study of the roughness of granite joints. In: Barla G (ed) Proc of the Eurock’96. Balkema, Rotterdam, p 117

Lee YH, Carr JR, Barr DJ, Haas CJ (1990) The fractal dimension as a measure of the roughness of rock discontinuity profiles. Int J Rock Mech Min Sci 27(6):453–464. https://doi.org/10.1016/0148-9062(90)90998-hCrossRef

Li Y, Huang R (2015) Relationship between joint roughness coefficient and fractal dimension of rock fracture surfaces. Int J Rock Mech Min Sci 75:15–22. https://doi.org/10.1016/j.ijrmms.2015.01.007CrossRef

Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput vis 2004(60):91–110CrossRef

Maerz NH, Franklin JA, Bennett CP (1990) Joint roughness measurement using shadow profilometry. Int J Rock Mech Min Sci 27(5):329–343. https://doi.org/10.1016/0148-9062(90)92708-mCrossRef

Mandelbrot BB (1977) Fractals – form, chance and dimension. Freeman, San Francisco, pp 1–94

Mohd-Nordin MM, Song K, Cho GC, Mohamed Z (2014) Long- wavelength elastic wave propagation across naturally fractured rock masses. J Rock Mech Rock Eng 2014(47):561–573. https://doi.org/10.1007/s00603-013-0448-xCrossRef

Myers NO (1962) Characterization of surface roughness. Wear 5:182–189CrossRef

Nilsson M, Wulkan F (2011) Determination of joint shear strength using photogrammetry. Luleå University of Technology, Luleå, Sweden

Paixão A, Muralha J, Resende R, Fortunato E (2022) Close-range photogrammetry for 3D rock joint roughness evaluation. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-022-02789-9CrossRef

Rahim IA (2018) Sample type of tilt testing and basic friction angle value for the crocker formation’s fine sandstone of Sabah. Malaysia. ASM Sci J 11(3):215–223 (SANREM)

Salvini R, Vanneschi C, Coggan JS, Mastrorocco G (2020) Evaluation of the use of UAV photogrammetry for rock discontinuity roughness characterization. Rock Mech Rock Eng 53(8):3699–3720. https://doi.org/10.1007/s00603-020-02130-2CrossRef

Shirono T, Kulatilake PHSW (1997) Accuracy of the spectral method in estimating fractal/spectral parameters for self-affine roughness profiles. Int J Rock Mech Min Sci 34(5):789–804. https://doi.org/10.1016/s0148-9062(96)00068-xCrossRef

Sirkiä J, Kallio P, Lakovlev D, Uotinen LKT (2016) Photogrammetric calculation of JRC for rock slope support design. Proceedings of the Eighth International Symposium on Ground Support in Mining and Underground Construction: Ground Support 2016, Lulea, Sweden. https://doi.org/10.13140/RG.2.2.21173.06880CrossRef

Tatone BSA, Grasselli G (2010) A new 2D discontinuity roughness parameter and its correlation with JRC. Int J Rock Mech Min Sci 47(8):1391–400. https://doi.org/10.1016/j.ijrmms.2010.06.006CrossRef

Tse R, Cruden DM (1979) Estimating joint roughness coefficients. Int J Rock Mech Min Sci 16(5):303–307. https://doi.org/10.1016/0148-9062(79)90241-9CrossRef

Turk N, Greig MJ, Dearman WR, Amin FF (1987) Characterization of joint surfaces by fractal dimension. Proceedings of the 28th US rock mechanics symposium, Tucson, pp 1223–1236

VisualSFM Version 0.5.22 (2014b) NVidia CUDA, Windows 64-bit (Wu et al. 2011; Wu 2013). http://ccwu.me/vsfm

Wang C, Wang L, Karakus M (2019) A new spectral analysis method for determining the joint roughness coefficient of rock joints. Int J Rock Mech Min Sci 2019(113):72–82. https://doi.org/10.1016/j.ijrmms.2018.11.009CrossRef

Westoby MJ, Brasington J, Glasser NF et al (2012) Structure-from-motion photogrammetry: a low-cost effective tool for geoscience applications. Geomorphology 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021CrossRef

Wu YX, Liu QS, Liu XY (2011) Study of relationship between joint roughness coefficient and statistical parameters. Chin J Rock Mech Eng 30:2593–2598 (in Chinese)

Xie HP, Pariseau WG (1994) Fractal estimation of rock joint roughness coefficient. Sci China 24(5):524–530

Yu X, Vayssade B (1991) Joint profiles and their roughness parameters. Int J Rock Mech Min Sci 28(4):333–336. https://doi.org/10.1016/0148-9062(91)90182-lCrossRef

Zhang G, Karakus M, Tang H, Ge Y, Zhang L (2014a) A new method estimating the 2D joint roughness coefficient for discontinuity surfaces in rock masses. Int J Rock Mech Min Sci 2014(72):191–198. https://doi.org/10.1016/j.ijrmms.2014.09.009CrossRef

Zhang JM, Tang ZC, Jiang JD, Zhan T (2014b) Research on relationship between statistical parameters and JRC. Sci Technol Eng 15(14):1–5 (in Chinese)

Zhao L, Huang D, Chen J, Wang X, Luo W, Zhu Z, Li D, Shi Zuo (2020) A practical photogrammetric workflow in the field of the construction of a 3D rock joint surface database. Eng Geol 279:105878. https://doi.org/10.1016/j.enggeo.2020.105878CrossRef

Zhou CB, Xiong WL (1996) Relation between joint roughness coefficient and fractal dimension. J Wuhan Univ Hydraul Elect Eng 29(5):1195–1197

Zimmerman RW, Bodvarsson GS (1996) Hydraulic conductivity of rock fractures. Transp Porous Media 23(1):1–30CrossRef

Titel: Determination of joint roughness coefficient using a cost-effective photogrammetry technique
verfasst von: Sakshi Rohilla
Resmi Sebastian
Publikationsdatum: 01.04.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 4/2023
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-023-03135-1

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