Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/1822/44912 |
Resumo: | This paper presents the effectiveness of soft computing algorithms in analyzing the bond behavior of fiber reinforced polymer (FRP) systems inserted in the cover of concrete elements, commonly known as the near-surface mounted (NSM) technique. It focuses on the use of Data Mining (DM) algorithms as an alternative to the existing guidelines’ models to predict the bond strength of NSM FRP systems. To ease and spread the use of DM algorithms, a web-based tool is presented. This tool was developed to allow an easy use of the DM prediction models presented in this work, where the user simply provides the values of the input variables, the same as those used by the guidelines, in order to get the predictions. The results presented herein show that the DM based models are robust and more accurate than the guidelines’ models and can be considered as a relevant alternative to those analytical methods. |
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Using data mining algorithms to predict the bond strength of NSM FRP systems in concreteNSMBondFRPGuidelinesData MiningEngenharia e Tecnologia::Engenharia CivilScience & TechnologyThis paper presents the effectiveness of soft computing algorithms in analyzing the bond behavior of fiber reinforced polymer (FRP) systems inserted in the cover of concrete elements, commonly known as the near-surface mounted (NSM) technique. It focuses on the use of Data Mining (DM) algorithms as an alternative to the existing guidelines’ models to predict the bond strength of NSM FRP systems. To ease and spread the use of DM algorithms, a web-based tool is presented. This tool was developed to allow an easy use of the DM prediction models presented in this work, where the user simply provides the values of the input variables, the same as those used by the guidelines, in order to get the predictions. The results presented herein show that the DM based models are robust and more accurate than the guidelines’ models and can be considered as a relevant alternative to those analytical methods.This work was supported by FEDER funds through the Operational Program for Competitiveness Factors - COMPETE and National Funds through FCT (Portuguese Foundation for Science and Technology) under the project CutInDur PTDC/ECM/112396/2009 (Ref. PTDC/ECM/112396/2009) and partly financed by the project POCI-01-0145-FEDER-007633. The first author wishes also to acknowledge the Grant No. SFRH/BD/87443/2012 provided by FCT.Elsevier Sci LtdUniversidade do MinhoCoelho, Mário Rui FreitasSena-Cruz, JoséNeves, Luís A. C.Pereira, MartaCortez, PauloMiranda, Tiago F. S.20162016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/44912engCoelho, M. R. F., Sena-Cruz, J. M., Neves, L. A. C., Pereira, M., Cortez, P., & Miranda, T. (2016). Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete. Construction and Building Materials, 126, 484-495. doi: 10.1016/j.conbuildmat.2016.09.0480950-06181879-052610.1016/j.conbuildmat.2016.09.048info: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:RCAAP2023-07-21T12:17:32Zoai:repositorium.sdum.uminho.pt:1822/44912Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:10:12.455142Repositório 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 |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
title |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
spellingShingle |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete Coelho, Mário Rui Freitas NSM Bond FRP Guidelines Data Mining Engenharia e Tecnologia::Engenharia Civil Science & Technology |
title_short |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
title_full |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
title_fullStr |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
title_full_unstemmed |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
title_sort |
Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
author |
Coelho, Mário Rui Freitas |
author_facet |
Coelho, Mário Rui Freitas Sena-Cruz, José Neves, Luís A. C. Pereira, Marta Cortez, Paulo Miranda, Tiago F. S. |
author_role |
author |
author2 |
Sena-Cruz, José Neves, Luís A. C. Pereira, Marta Cortez, Paulo Miranda, Tiago F. S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Coelho, Mário Rui Freitas Sena-Cruz, José Neves, Luís A. C. Pereira, Marta Cortez, Paulo Miranda, Tiago F. S. |
dc.subject.por.fl_str_mv |
NSM Bond FRP Guidelines Data Mining Engenharia e Tecnologia::Engenharia Civil Science & Technology |
topic |
NSM Bond FRP Guidelines Data Mining Engenharia e Tecnologia::Engenharia Civil Science & Technology |
description |
This paper presents the effectiveness of soft computing algorithms in analyzing the bond behavior of fiber reinforced polymer (FRP) systems inserted in the cover of concrete elements, commonly known as the near-surface mounted (NSM) technique. It focuses on the use of Data Mining (DM) algorithms as an alternative to the existing guidelines’ models to predict the bond strength of NSM FRP systems. To ease and spread the use of DM algorithms, a web-based tool is presented. This tool was developed to allow an easy use of the DM prediction models presented in this work, where the user simply provides the values of the input variables, the same as those used by the guidelines, in order to get the predictions. The results presented herein show that the DM based models are robust and more accurate than the guidelines’ models and can be considered as a relevant alternative to those analytical methods. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z |
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/1822/44912 |
url |
http://hdl.handle.net/1822/44912 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Coelho, M. R. F., Sena-Cruz, J. M., Neves, L. A. C., Pereira, M., Cortez, P., & Miranda, T. (2016). Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete. Construction and Building Materials, 126, 484-495. doi: 10.1016/j.conbuildmat.2016.09.048 0950-0618 1879-0526 10.1016/j.conbuildmat.2016.09.048 |
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 |
Elsevier Sci Ltd |
publisher.none.fl_str_mv |
Elsevier Sci Ltd |
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 |
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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 |
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1799132529699061760 |