Estimation of the rock deformation modulus and RMR based on data mining techniques
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/20085 |
Resumo: | In this work Data Mining tools are used to develop new and innovative models for the estimation of the rock deformation modulus and the Rock Mass Rating (RMR). A database published by Chun et al. (2008) was used to develop these models. The parameters of the database were the depth, the weightings of the RMR system related to the uniaxial compressive strength (UCS), the rock quality designation (RQD), the joint spacing (JS), the joint condition (JC), the groundwater condition (GWC) and the discontinuity orientation adjustment (DOA), the RMR and the deformation modulus. As a modelling tool the R program environment was used to apply these advanced techniques. Several algorithms were tested and analysed using different sets of input parameters. It was possible to develop new models to predict the rock deformation modulus and the RMR with improved accuracy and, additionally, allowed to have an insight of the importance of the different input parameters. |
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Estimation of the rock deformation modulus and RMR based on data mining techniquesDeformation modulusRMRData miningMachine learningScience & TechnologyIn this work Data Mining tools are used to develop new and innovative models for the estimation of the rock deformation modulus and the Rock Mass Rating (RMR). A database published by Chun et al. (2008) was used to develop these models. The parameters of the database were the depth, the weightings of the RMR system related to the uniaxial compressive strength (UCS), the rock quality designation (RQD), the joint spacing (JS), the joint condition (JC), the groundwater condition (GWC) and the discontinuity orientation adjustment (DOA), the RMR and the deformation modulus. As a modelling tool the R program environment was used to apply these advanced techniques. Several algorithms were tested and analysed using different sets of input parameters. It was possible to develop new models to predict the rock deformation modulus and the RMR with improved accuracy and, additionally, allowed to have an insight of the importance of the different input parameters.Fundação para a Ciência e a Tecnologia (FCT)SpringerUniversidade do MinhoMartins, Francisco F.Miranda, Tiago F. S.2012-022012-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/20085eng0960-318210.1007/s10706-012-9498-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T11:55:14Zoai:repositorium.sdum.uminho.pt:1822/20085Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:44:45.637286Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
title |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
spellingShingle |
Estimation of the rock deformation modulus and RMR based on data mining techniques Martins, Francisco F. Deformation modulus RMR Data mining Machine learning Science & Technology |
title_short |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
title_full |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
title_fullStr |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
title_full_unstemmed |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
title_sort |
Estimation of the rock deformation modulus and RMR based on data mining techniques |
author |
Martins, Francisco F. |
author_facet |
Martins, Francisco F. Miranda, Tiago F. S. |
author_role |
author |
author2 |
Miranda, Tiago F. S. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Martins, Francisco F. Miranda, Tiago F. S. |
dc.subject.por.fl_str_mv |
Deformation modulus RMR Data mining Machine learning Science & Technology |
topic |
Deformation modulus RMR Data mining Machine learning Science & Technology |
description |
In this work Data Mining tools are used to develop new and innovative models for the estimation of the rock deformation modulus and the Rock Mass Rating (RMR). A database published by Chun et al. (2008) was used to develop these models. The parameters of the database were the depth, the weightings of the RMR system related to the uniaxial compressive strength (UCS), the rock quality designation (RQD), the joint spacing (JS), the joint condition (JC), the groundwater condition (GWC) and the discontinuity orientation adjustment (DOA), the RMR and the deformation modulus. As a modelling tool the R program environment was used to apply these advanced techniques. Several algorithms were tested and analysed using different sets of input parameters. It was possible to develop new models to predict the rock deformation modulus and the RMR with improved accuracy and, additionally, allowed to have an insight of the importance of the different input parameters. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-02 2012-02-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/20085 |
url |
http://hdl.handle.net/1822/20085 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0960-3182 10.1007/s10706-012-9498-1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799132197489213440 |