Abstract
The application of conventional techniques, such as kriging, to model rock mass is limited because rock mass spatial variability and heterogeneity are not considered in such techniques. In this context, as an alternative solution, the application of the Gaussian simulation technique to simulate rock mass spatial heterogeneity based on the rock mass rating (RMR) classification is proposed. This research proposes a methodology that includes a variographic analysis of the RMR in different directions to determine its anisotropic behavior. In the case study of an underground deposit in Peru, the geomechanical record data compiled in the field were used. A total of 10 simulations were conducted, with approximately 6 million values for each simulation. These were calculated, verified, and an absolute mean error of only 3.82% was estimated. It is acceptable when compared with the value of 22.15% obtained with kriging.
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References
Barton, N., Lien, R., Lunde, J.: Engineering classification of rock masses for the design of tunnel support. Rock Mech. Felsmechanik Mécanique des Roches 6(4), 189–236 (1974)
Bieniawski, Z.T.: Engineering classification of jointed rock masses. Civ. Eng. S. Afr. 15(12), 335–343 (1973)
Bieniawski: Exploration for rock engineering. 97–106 (1976)
Deere, D.U.: The rock quality designation (RQD) index in practice. 91–101 (1988)
Cartaya, M.: Caracterizacion geomecánica de macizos rocosos en obras subterráneas de la región oriental del país (2006)
Ferrari, F., Apuani, T., Giani, G.P.: Rock mass rating spatial estimation by geostatistical analysis. Int. J. Rock Mech. Min. Sci. 70, 162–176 (2014)
Paithankar, A., Chatterjee, S.: Grade and tonnage uncertainty analysis of an african copper deposit using multiple-point geostatistics and sequential gaussian simulation. Nat. Resour. Res. 27(4), 419–436 (2018)
Kova, Z., Cvetkovi, M., Parlov, J.: Gaussian simulation of nitrate concentration distribution in the Zagreb aquifer. J. Maps 13(2), 727–732 (2017)
Perez, M.S., et al.: Estudio comparativo entre sistemas de clasificación geomecánica en un depósito tipo Pórfido. Boletín Ciencias la Tierra 43, 34–44 (2018)
Eivazy, H., Esmaieli, K., Jean, R.: Modelling geomechanical heterogeneity of rock masses using direct and indirect geostatistical conditional simulation methods. Rock Mech. Rock Eng. 50(12), 93–103 (2017)
Pinheiro, M., Emery, X., Miranda, T., Vallejos, J.: Truncated gaussian simulation to map the spatial heterogeneity of rock mass rating. Rock Mech. Rock Eng. 49(8), 3371–3376 (2016)
Kang, N., Yongzhi, W., Hui, Z., Sheng, Y.: Construction and application of 3D geological models for attribute-oriented. J. Appl. Sci. Eng. 18(4), 315–322 (2015)
Vatcher, J., McKinnon, S.D., Sjöberg, J.: Developing 3-D mine-scale geomechanical models in complex geological environments, as applied to the Kiirunavaara Mine. Eng. Geol. 203, 140–150 (2016)
Pinheiro, M., Vallejos, J., Miranda, T., Emery, X.: Geostatistical simulation to map the spatial heterogeneity of geomechanical parameters: a case study with rock mass rating. Eng. Geol. 205, 93–103 (2016)
Safikhani, M., Asghari, O., Emery, X.: Assessing the accuracy of sequential gaussian simulation through statistical testing. Stoch. Environ. Res. Risk Assess. 31(2), 523–533 (2017)
Ozturk, H., Erkayaoglu, M.: Interpretation of variability of rock mass rating by geostatistical analysis: a case study in Western Turkey. Arab. J. Geosci. 11(13), 344 (2018)
Novak Zelenika, K., Vidaček, R., Ilijaš, T., Pavić, P.: Application of the deterministical and stochastical geostatistical methods in petrophysical modeling, case study Upper Pannonian reservoir, Sava depression. Geol. Croat. 70(2), 105–114 (2017)
Mayer, J.M., Stead, D.: A comparison of traditional, step-path, and geostatistical techniques in the stability analysis of a large open pit. Rock Mech. Rock Eng. 50(4), 927–949 (2017)
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Rodriguez-Vilca, J., Paucar-Vilcañaupa, J., Pehovaz-Alvarez, H., Raymundo, C., Mamani-Macedo, N., Moguerza, J.M. (2020). Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines. In: Kantola, J., Nazir, S., Salminen, V. (eds) Advances in Human Factors, Business Management and Leadership. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1209. Springer, Cham. https://doi.org/10.1007/978-3-030-50791-6_44
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DOI: https://doi.org/10.1007/978-3-030-50791-6_44
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