Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping

Detalhes bibliográficos
Autor(a) principal: Carvalho, Matheus Gomes
Data de Publicação: 2022
Outros Autores: Barreto, Eduardo Matthews do Rego, Ferreira, José Ailton da Costa, França, Fagner Alexandre Nunes de, Freitas Neto, Osvaldo de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/24506
Resumo: The investigation of soils shear strength is necessary in many Geotechnical Engineering applications, e.g. foundations, slope stability and retaining walls design. It is usually conducted by means of field and/or laboratory standard tests. In both cases, they are time-consuming and require specialized personnel to be performed. Several studies are found in the literature in which artificial intelligence tools are used as an alternative to those tests. This paper presents a systematic mapping review on this subject, in which algorithms types and geotechnical parameters needed to estimate soils shear strength are identified, based on data extracted from the literature. It was possible to list 17 techniques applied to different soil types. The results from these studies have been in good agreement with data from real laboratory and/or field tests. This demonstrates the potential of application of artificial intelligence to estimate soils shear strength.
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spelling Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mappingAplicaciones de la inteligencia artificial en la determinación de los parámetros de resistencia al cizallamiento del suelo: un mapeo sistemático de la literaturaAplicações de inteligência artificial na determinação de parâmetros de resistência ao cisalhamento do solo: um mapeamento sistemático da literatura Systematic mappingShear strengthArtificial intelligence.Mapeamento sistemáticoResistência ao cisalhamentoInteligência artificial.Mapeo sistemáticoResistencia al corteInteligencia artificial.The investigation of soils shear strength is necessary in many Geotechnical Engineering applications, e.g. foundations, slope stability and retaining walls design. It is usually conducted by means of field and/or laboratory standard tests. In both cases, they are time-consuming and require specialized personnel to be performed. Several studies are found in the literature in which artificial intelligence tools are used as an alternative to those tests. This paper presents a systematic mapping review on this subject, in which algorithms types and geotechnical parameters needed to estimate soils shear strength are identified, based on data extracted from the literature. It was possible to list 17 techniques applied to different soil types. The results from these studies have been in good agreement with data from real laboratory and/or field tests. This demonstrates the potential of application of artificial intelligence to estimate soils shear strength.La investigación de la resistencia al corte de los suelos es una tarea común en varios proyectos de ingeniería geotécnica, por ejemplo, cimentaciones, estabilidad de taludes y estructuras de contención. Por lo general, se lleva a cabo mediante pruebas estandarizadas de campo y / o laboratorio. En ambos casos, es una tarea que demanda tiempo y tiene un costo asociado. En la literatura se pueden encontrar varios estudios en los que se utilizan herramientas de inteligencia artificial como alternativa a la realización de estas pruebas. Este artículo presenta un mapeo sistemático de la literatura sobre este tema. Los tipos de algoritmos y parámetros geotécnicos utilizados para estimar la resistencia al corte del suelo se identificaron con base en datos extraídos de la literatura. Fue posible enumerar 17 técnicas aplicadas a diferentes tipos de suelo. Los resultados de estos estudios están de acuerdo con los datos obtenidos a través de pruebas geotécnicas, de laboratorio y de campo reales. Esto demuestra el potencial de usar inteligencia artificial para estimar la resistencia al corte de los suelos.A investigação da resistência ao cisalhamento dos solos é tarefa corriqueira em diversos projetos de Engenharia Geotécnica, e.g. fundações, estabilidade de taludes e estruturas de contenção. Ela é usualmente conduzida por meio de ensaios padronizados de campo e/ou laboratório. Nos dois casos, trata-se de uma tarefa que demanda tempo e possui um custo associado a ela. Diversos estudos podem ser encontrados na literatura em que ferramentas de inteligência artificial são usadas como alternativa à execução desses ensaios. Este artigo apresenta um mapeamento sistemático da literatura sobre esse assunto. Os tipos de algoritmo e os parâmetros geotécnicos empregados para estimar a resistência ao cisalhamento do solo foram identificados com base nos dados extraídos da literatura. Foi possível listar 17 técnicas aplicadas a diferentes tipos de solo. Os resultados desses estudos encontram-se de acordo com dados obtidos por meio de ensaios geotécnicos reais, de laboratório e de campo. Isso demonstra o potencial de uso de inteligência artificial para estimar a resistência ao cisalhamento dos solos.Research, Society and Development2022-01-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2450610.33448/rsd-v11i1.24506Research, Society and Development; Vol. 11 No. 1; e27711124506Research, Society and Development; Vol. 11 Núm. 1; e27711124506Research, Society and Development; v. 11 n. 1; e277111245062525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/24506/21877Copyright (c) 2022 Matheus Gomes Carvalho; Eduardo Matthews do Rego Barreto; José Ailton da Costa Ferreira; Fagner Alexandre Nunes de França; Osvaldo de Freitas Netohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCarvalho, Matheus Gomes Barreto, Eduardo Matthews do RegoFerreira, José Ailton da CostaFrança, Fagner Alexandre Nunes deFreitas Neto, Osvaldo de2022-01-16T18:08:18Zoai:ojs.pkp.sfu.ca:article/24506Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:43:02.155618Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
Aplicaciones de la inteligencia artificial en la determinación de los parámetros de resistencia al cizallamiento del suelo: un mapeo sistemático de la literatura
Aplicações de inteligência artificial na determinação de parâmetros de resistência ao cisalhamento do solo: um mapeamento sistemático da literatura
title Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
spellingShingle Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
Carvalho, Matheus Gomes
Systematic mapping
Shear strength
Artificial intelligence.
Mapeamento sistemático
Resistência ao cisalhamento
Inteligência artificial.
Mapeo sistemático
Resistencia al corte
Inteligencia artificial.
title_short Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
title_full Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
title_fullStr Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
title_full_unstemmed Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
title_sort Applications of artificial intelligence in determining soil shear strength parameters: a systematic literature mapping
author Carvalho, Matheus Gomes
author_facet Carvalho, Matheus Gomes
Barreto, Eduardo Matthews do Rego
Ferreira, José Ailton da Costa
França, Fagner Alexandre Nunes de
Freitas Neto, Osvaldo de
author_role author
author2 Barreto, Eduardo Matthews do Rego
Ferreira, José Ailton da Costa
França, Fagner Alexandre Nunes de
Freitas Neto, Osvaldo de
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Carvalho, Matheus Gomes
Barreto, Eduardo Matthews do Rego
Ferreira, José Ailton da Costa
França, Fagner Alexandre Nunes de
Freitas Neto, Osvaldo de
dc.subject.por.fl_str_mv Systematic mapping
Shear strength
Artificial intelligence.
Mapeamento sistemático
Resistência ao cisalhamento
Inteligência artificial.
Mapeo sistemático
Resistencia al corte
Inteligencia artificial.
topic Systematic mapping
Shear strength
Artificial intelligence.
Mapeamento sistemático
Resistência ao cisalhamento
Inteligência artificial.
Mapeo sistemático
Resistencia al corte
Inteligencia artificial.
description The investigation of soils shear strength is necessary in many Geotechnical Engineering applications, e.g. foundations, slope stability and retaining walls design. It is usually conducted by means of field and/or laboratory standard tests. In both cases, they are time-consuming and require specialized personnel to be performed. Several studies are found in the literature in which artificial intelligence tools are used as an alternative to those tests. This paper presents a systematic mapping review on this subject, in which algorithms types and geotechnical parameters needed to estimate soils shear strength are identified, based on data extracted from the literature. It was possible to list 17 techniques applied to different soil types. The results from these studies have been in good agreement with data from real laboratory and/or field tests. This demonstrates the potential of application of artificial intelligence to estimate soils shear strength.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-06
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/24506
10.33448/rsd-v11i1.24506
url https://rsdjournal.org/index.php/rsd/article/view/24506
identifier_str_mv 10.33448/rsd-v11i1.24506
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/24506/21877
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 11 No. 1; e27711124506
Research, Society and Development; Vol. 11 Núm. 1; e27711124506
Research, Society and Development; v. 11 n. 1; e27711124506
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
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