Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
Detalles Bibliográficos
Autores: Rodriguez-Vilca, Juliet, Paucar-Vilcañaupa, Jose, Pehovaz-Alvarez, Humberto, Raymundo, Carlos, Mamani-Macedo, Nestor, Moguerza, Javier M.
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/656171
Enlace del recurso:http://hdl.handle.net/10757/656171
Nivel de acceso:acceso embargado
Materia:Gaussian simulation
Geomechanical uncertainty
Geostatistics
RMR
Uncertainty analysis
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oai_identifier_str oai:repositorioacademico.upc.edu.pe:10757/656171
network_acronym_str UUPC
network_name_str UPC-Institucional
repository_id_str 2670
dc.title.en_US.fl_str_mv Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
title Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
spellingShingle Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
Rodriguez-Vilca, Juliet
Gaussian simulation
Geomechanical uncertainty
Geostatistics
RMR
Uncertainty analysis
title_short Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
title_full Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
title_fullStr Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
title_full_unstemmed Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
title_sort Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
author Rodriguez-Vilca, Juliet
author_facet Rodriguez-Vilca, Juliet
Paucar-Vilcañaupa, Jose
Pehovaz-Alvarez, Humberto
Raymundo, Carlos
Mamani-Macedo, Nestor
Moguerza, Javier M.
author_role author
author2 Paucar-Vilcañaupa, Jose
Pehovaz-Alvarez, Humberto
Raymundo, Carlos
Mamani-Macedo, Nestor
Moguerza, Javier M.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Rodriguez-Vilca, Juliet
Paucar-Vilcañaupa, Jose
Pehovaz-Alvarez, Humberto
Raymundo, Carlos
Mamani-Macedo, Nestor
Moguerza, Javier M.
dc.subject.en_US.fl_str_mv Gaussian simulation
Geomechanical uncertainty
Geostatistics
RMR
Uncertainty analysis
topic Gaussian simulation
Geomechanical uncertainty
Geostatistics
RMR
Uncertainty analysis
description El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2021-05-27T12:37:27Z
dc.date.available.none.fl_str_mv 2021-05-27T12:37:27Z
dc.date.issued.fl_str_mv 2020-01-01
dc.type.en_US.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.issn.none.fl_str_mv 21945357
dc.identifier.doi.none.fl_str_mv 10.1007/978-3-030-50791-6_44
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/656171
dc.identifier.eissn.none.fl_str_mv 21945365
dc.identifier.journal.en_US.fl_str_mv Advances in Intelligent Systems and Computing
dc.identifier.eid.none.fl_str_mv 2-s2.0-85088229196
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85088229196
dc.identifier.isni.none.fl_str_mv 0000 0001 2196 144X
identifier_str_mv 21945357
10.1007/978-3-030-50791-6_44
21945365
Advances in Intelligent Systems and Computing
2-s2.0-85088229196
SCOPUS_ID:85088229196
0000 0001 2196 144X
url http://hdl.handle.net/10757/656171
dc.language.iso.en_US.fl_str_mv eng
language eng
dc.relation.url.en_US.fl_str_mv https://link.springer.com/chapter/10.1007/978-3-030-50791-6_44
dc.rights.en_US.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.en_US.fl_str_mv application/html
dc.publisher.en_US.fl_str_mv Springer
dc.source.es_PE.fl_str_mv Universidad Peruana de Ciencias Aplicadas (UPC)
Repositorio Académico - UPC
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
instacron_str UPC
institution UPC
reponame_str UPC-Institucional
collection UPC-Institucional
dc.source.journaltitle.none.fl_str_mv Advances in Intelligent Systems and Computing
dc.source.volume.none.fl_str_mv 1209 AISC
dc.source.beginpage.none.fl_str_mv 342
dc.source.endpage.none.fl_str_mv 349
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/656171/1/license.txt
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio académico upc
repository.mail.fl_str_mv upc@openrepository.com
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spelling 25d0a09e98f949731d7e072711b650533002c5717659888259491b3d506009377e4300db417f44df9fdd9c28b2461e37028dfcf1b29165990ab4ce165cbf28f5e4ccd95003c4929de31ab03204ce4b92da8cfadf8500eab45c535b46774d53b19b94802aaba8500Rodriguez-Vilca, JulietPaucar-Vilcañaupa, JosePehovaz-Alvarez, HumbertoRaymundo, CarlosMamani-Macedo, NestorMoguerza, Javier M.2021-05-27T12:37:27Z2021-05-27T12:37:27Z2020-01-012194535710.1007/978-3-030-50791-6_44http://hdl.handle.net/10757/65617121945365Advances in Intelligent Systems and Computing2-s2.0-85088229196SCOPUS_ID:850882291960000 0001 2196 144XEl texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.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.application/htmlengSpringerhttps://link.springer.com/chapter/10.1007/978-3-030-50791-6_44info:eu-repo/semantics/embargoedAccessUniversidad Peruana de Ciencias Aplicadas (UPC)Repositorio Académico - UPCAdvances in Intelligent Systems and Computing1209 AISC342349reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCGaussian simulationGeomechanical uncertaintyGeostatisticsRMRUncertainty analysisMethod for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Minesinfo:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/656171/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/656171oai:repositorioacademico.upc.edu.pe:10757/6561712021-05-27 12:37:28.691Repositorio académico upcupc@openrepository.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Method for the Interpretation of RMR Variability Using Gaussian Simulation to Reduce the Uncertainty in Estimations of Geomechanical Models of Underground Mines
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