Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming

Detalhes bibliográficos
Autor(a) principal: Tariq, Moiz
Data de Publicação: 2022
Outros Autores: Khan, Azam, Ullah, Asad, Shayanfar, Javad, Niaz, Momina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/79911
Resumo: In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.
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spelling Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programminggene expression programmingreinforced concretesteel fiber reinforced concreteshear strengthEngenharia e Tecnologia::Engenharia CivilScience & TechnologyIn this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.National University of Science and TechnologyMultidisciplinary Digital Publishing InstituteUniversidade do MinhoTariq, MoizKhan, AzamUllah, AsadShayanfar, JavadNiaz, Momina2022-05-242022-05-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/79911eng1996-194410.3390/ma15113758https://www.mdpi.com/1996-1944/15/11/3758info: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:RCAAP2023-07-21T12:29:03Zoai:repositorium.sdum.uminho.pt:1822/79911Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:23:57.547107Repositório 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 Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
title Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
spellingShingle Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
Tariq, Moiz
gene expression programming
reinforced concrete
steel fiber reinforced concrete
shear strength
Engenharia e Tecnologia::Engenharia Civil
Science & Technology
title_short Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
title_full Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
title_fullStr Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
title_full_unstemmed Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
title_sort Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
author Tariq, Moiz
author_facet Tariq, Moiz
Khan, Azam
Ullah, Asad
Shayanfar, Javad
Niaz, Momina
author_role author
author2 Khan, Azam
Ullah, Asad
Shayanfar, Javad
Niaz, Momina
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Tariq, Moiz
Khan, Azam
Ullah, Asad
Shayanfar, Javad
Niaz, Momina
dc.subject.por.fl_str_mv gene expression programming
reinforced concrete
steel fiber reinforced concrete
shear strength
Engenharia e Tecnologia::Engenharia Civil
Science & Technology
topic gene expression programming
reinforced concrete
steel fiber reinforced concrete
shear strength
Engenharia e Tecnologia::Engenharia Civil
Science & Technology
description In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-24
2022-05-24T00:00:00Z
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 https://hdl.handle.net/1822/79911
url https://hdl.handle.net/1822/79911
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1996-1944
10.3390/ma15113758
https://www.mdpi.com/1996-1944/15/11/3758
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 Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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
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