Improved shear strength prediction model of steel fiber reinforced concrete beams by adopting gene expression programming
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799132717505314816 |