TY - GEN
T1 - Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
AU - Rodriguez-Vilca, Juliet
AU - Paucar-Vilcañaupa, Jose
AU - Pehovaz-Alvarez, Humberto
AU - Raymundo, Carlos
AU - Mamani-Macedo, Nestor
AU - Moguerza, Javier M.
N1 - Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Gaussian simulation
KW - Geomechanical uncertainty
KW - Geostatistics
KW - RMR
KW - Uncertainty analysis
UR - https://www.scopus.com/pages/publications/85088229196
U2 - 10.1007/978-3-030-50791-6_44
DO - 10.1007/978-3-030-50791-6_44
M3 - Contribución a la conferencia
AN - SCOPUS:85088229196
SN - 9783030507909
T3 - Advances in Intelligent Systems and Computing
SP - 342
EP - 349
BT - Advances in Human Factors, Business Management and Leadership - Proceedings of the AHFE 2020 Virtual Conferences on Human Factors, Business Management and Society, and Human Factors in Management and Leadership
A2 - Kantola, Jussi Ilari
A2 - Nazir, Salman
A2 - Salminen, Vesa
PB - Springer
T2 - AHFE Virtual Conference on Human Factors, Business Management and Society, and the International Conference on Management and Leadership, 2020
Y2 - 16 July 2020 through 20 July 2020
ER -