Soft computing-based models for the prediction of masonry compressive strength

Detalhes bibliográficos
Autor(a) principal: Asteris, Panagiotis G.
Data de Publicação: 2021
Outros Autores: Lourenço, Paulo B., Hajihassani, Mohsen, Adami, Chrissy-Elpida N., Lemonis, Minas E., Skentou, Athanasia D., Marques, Rui Filipe Pedreira, Hoang Nguyen, Rodrigues, Hugo, Varum, Humberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/77245
Resumo: Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.
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spelling Soft computing-based models for the prediction of masonry compressive strengthArtificial neural networksGenetic programmingMachine learningMasonryMetaheuristic algorithmsCompressive strengthScience & TechnologyMasonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.MU - Malayer University(undefined)ElsevierUniversidade do MinhoAsteris, Panagiotis G.Lourenço, Paulo B.Hajihassani, MohsenAdami, Chrissy-Elpida N.Lemonis, Minas E.Skentou, Athanasia D.Marques, Rui Filipe PedreiraHoang NguyenRodrigues, HugoVarum, Humberto2021-10-052021-10-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/77245engAsteris, P. G., Lourenço, P. B., Hajihassani, M., Adami, C.-E. N., Lemonis, M. E., Skentou, A. D., … Varum, H. (2021, December). Soft computing-based models for the prediction of masonry compressive strength. Engineering Structures. Elsevier BV. http://doi.org/10.1016/j.engstruct.2021.1132760141-029610.1016/j.engstruct.2021.113276113276https://www.sciencedirect.com/science/article/pii/S0141029621013997info: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:RCAAP2024-01-13T01:23:56Zoai:repositorium.sdum.uminho.pt:1822/77245Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:57:55.108119Repositório 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 Soft computing-based models for the prediction of masonry compressive strength
title Soft computing-based models for the prediction of masonry compressive strength
spellingShingle Soft computing-based models for the prediction of masonry compressive strength
Asteris, Panagiotis G.
Artificial neural networks
Genetic programming
Machine learning
Masonry
Metaheuristic algorithms
Compressive strength
Science & Technology
title_short Soft computing-based models for the prediction of masonry compressive strength
title_full Soft computing-based models for the prediction of masonry compressive strength
title_fullStr Soft computing-based models for the prediction of masonry compressive strength
title_full_unstemmed Soft computing-based models for the prediction of masonry compressive strength
title_sort Soft computing-based models for the prediction of masonry compressive strength
author Asteris, Panagiotis G.
author_facet Asteris, Panagiotis G.
Lourenço, Paulo B.
Hajihassani, Mohsen
Adami, Chrissy-Elpida N.
Lemonis, Minas E.
Skentou, Athanasia D.
Marques, Rui Filipe Pedreira
Hoang Nguyen
Rodrigues, Hugo
Varum, Humberto
author_role author
author2 Lourenço, Paulo B.
Hajihassani, Mohsen
Adami, Chrissy-Elpida N.
Lemonis, Minas E.
Skentou, Athanasia D.
Marques, Rui Filipe Pedreira
Hoang Nguyen
Rodrigues, Hugo
Varum, Humberto
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Asteris, Panagiotis G.
Lourenço, Paulo B.
Hajihassani, Mohsen
Adami, Chrissy-Elpida N.
Lemonis, Minas E.
Skentou, Athanasia D.
Marques, Rui Filipe Pedreira
Hoang Nguyen
Rodrigues, Hugo
Varum, Humberto
dc.subject.por.fl_str_mv Artificial neural networks
Genetic programming
Machine learning
Masonry
Metaheuristic algorithms
Compressive strength
Science & Technology
topic Artificial neural networks
Genetic programming
Machine learning
Masonry
Metaheuristic algorithms
Compressive strength
Science & Technology
description Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.
publishDate 2021
dc.date.none.fl_str_mv 2021-10-05
2021-10-05T00:00:00Z
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/1822/77245
url https://hdl.handle.net/1822/77245
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Asteris, P. G., Lourenço, P. B., Hajihassani, M., Adami, C.-E. N., Lemonis, M. E., Skentou, A. D., … Varum, H. (2021, December). Soft computing-based models for the prediction of masonry compressive strength. Engineering Structures. Elsevier BV. http://doi.org/10.1016/j.engstruct.2021.113276
0141-0296
10.1016/j.engstruct.2021.113276
113276
https://www.sciencedirect.com/science/article/pii/S0141029621013997
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
publisher.none.fl_str_mv Elsevier
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
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