An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles

Detalhes bibliográficos
Autor(a) principal: Ozelim, Luan Carlos de Sena Monteiro
Data de Publicação: 2023
Outros Autores: Campos, Darym Júnior Ferrari de, Cavalcante, André Luís Brasil, Carvalho, José Camapum de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UnB
Texto Completo: http://repositorio2.unb.br/jspui/handle/10482/46679
https://doi.org/10.3390/axioms12040340
https://orcid.org/0000-0002-2581-0486
https://orcid.org/0000-0002-7128-3752
https://orcid.org/0000-0003-4980-1450
https://orcid.org/0000-0003-4155-4694
Resumo: Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should understand how to take advantage of ordinary execution procedures to obtain the parameters of interest. Sensors are key components in engineering big data frameworks, as they provide a large number of valuable measured data. In particular, the building process (excavation and concreting) of continuous flight auger piles (CFAPs) can be fully monitored by collecting data from sensors in the drilling machine. This makes this type of pile an ideal candidate to utilize a big data methodology to indirectly obtain some constitutive parameters of the soil being drilled. Thus, in the present paper, the drilling process of CFAPs is modeled by a new physical model which predicts the energy spending during the execution of this type of pile. This new model relies on other fundamental properties of the soils drilled, such as unit weight, cohesion and internal friction angle. In order to show the applicability of the big data methodological framework hereby developed, a case study was conducted. A work site in Brasília-DF, Brazil, was studied and the execution of three CFAPs was monitored. Soil surveys were carried out to identify the soil strata in the site and, therefore, to validate the estimates of Young’s moduli provided by the new formulas. The 95% confidence intervals of Young’s moduli obtained for silty clay, clayey silt and silt were, in MPa, [14.56, 19.11], [12.26, 16.88] and [19.65, 26.11], respectively. These intervals are consistent with literature reports for the following materials: stiff to very stiff clays with low-medium plasticity, medium silts with slight plasticity, and stiff to very stiff silts with low plasticity, respectively. These were the types of materials observed during the site surveys; therefore, the results obtained are consistent with literature reports as well as with field surveys. This new framework may be useful to provide real-time estimates of the drilled soil’s parameters, as well as updating CFAPs designs during their execution. This way, sustainable designs can be achieved, where substrata materials are better characterized, avoiding over-designed structures.
id UNB_76e4762e80563958937e8b6b0b9dad76
oai_identifier_str oai:repositorio.unb.br:10482/46679
network_acronym_str UNB
network_name_str Repositório Institucional da UnB
repository_id_str
spelling An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger pilesBig dataEngenharia de fundaçõesEstacaria (Engenharia civil)Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should understand how to take advantage of ordinary execution procedures to obtain the parameters of interest. Sensors are key components in engineering big data frameworks, as they provide a large number of valuable measured data. In particular, the building process (excavation and concreting) of continuous flight auger piles (CFAPs) can be fully monitored by collecting data from sensors in the drilling machine. This makes this type of pile an ideal candidate to utilize a big data methodology to indirectly obtain some constitutive parameters of the soil being drilled. Thus, in the present paper, the drilling process of CFAPs is modeled by a new physical model which predicts the energy spending during the execution of this type of pile. This new model relies on other fundamental properties of the soils drilled, such as unit weight, cohesion and internal friction angle. In order to show the applicability of the big data methodological framework hereby developed, a case study was conducted. A work site in Brasília-DF, Brazil, was studied and the execution of three CFAPs was monitored. Soil surveys were carried out to identify the soil strata in the site and, therefore, to validate the estimates of Young’s moduli provided by the new formulas. The 95% confidence intervals of Young’s moduli obtained for silty clay, clayey silt and silt were, in MPa, [14.56, 19.11], [12.26, 16.88] and [19.65, 26.11], respectively. These intervals are consistent with literature reports for the following materials: stiff to very stiff clays with low-medium plasticity, medium silts with slight plasticity, and stiff to very stiff silts with low plasticity, respectively. These were the types of materials observed during the site surveys; therefore, the results obtained are consistent with literature reports as well as with field surveys. This new framework may be useful to provide real-time estimates of the drilled soil’s parameters, as well as updating CFAPs designs during their execution. This way, sustainable designs can be achieved, where substrata materials are better characterized, avoiding over-designed structures.Faculdade de Tecnologia (FT)Departamento de Engenharia Civil e Ambiental (FT ENC)MDPIUniversity of Brasilia, Department of Civil and Environmental EngineeringOzelim, Luan Carlos de Sena MonteiroCampos, Darym Júnior Ferrari deCavalcante, André Luís BrasilCarvalho, José Camapum de2023-10-16T14:15:54Z2023-10-16T14:15:54Z2023-03-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfOZELIM, Luan Carlos de Sena Monteiro et al. An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles. Axioms, v. 12, n. 4, 340, 2023. DOI: https://doi.org/10.3390/axioms12040340. Disponível em: https://www.mdpi.com/2075-1680/12/4/340. Acesso em: 16 out. 2023.http://repositorio2.unb.br/jspui/handle/10482/46679https://doi.org/10.3390/axioms12040340https://orcid.org/0000-0002-2581-0486https://orcid.org/0000-0002-7128-3752https://orcid.org/0000-0003-4980-1450https://orcid.org/0000-0003-4155-4694engCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UnBinstname:Universidade de Brasília (UnB)instacron:UNB2023-10-16T14:15:54Zoai:repositorio.unb.br:10482/46679Repositório InstitucionalPUBhttps://repositorio.unb.br/oai/requestrepositorio@unb.bropendoar:2023-10-16T14:15:54Repositório Institucional da UnB - Universidade de Brasília (UnB)false
dc.title.none.fl_str_mv An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
title An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
spellingShingle An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
Ozelim, Luan Carlos de Sena Monteiro
Big data
Engenharia de fundações
Estacaria (Engenharia civil)
title_short An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
title_full An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
title_fullStr An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
title_full_unstemmed An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
title_sort An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles
author Ozelim, Luan Carlos de Sena Monteiro
author_facet Ozelim, Luan Carlos de Sena Monteiro
Campos, Darym Júnior Ferrari de
Cavalcante, André Luís Brasil
Carvalho, José Camapum de
author_role author
author2 Campos, Darym Júnior Ferrari de
Cavalcante, André Luís Brasil
Carvalho, José Camapum de
author2_role author
author
author
dc.contributor.none.fl_str_mv University of Brasilia, Department of Civil and Environmental Engineering
dc.contributor.author.fl_str_mv Ozelim, Luan Carlos de Sena Monteiro
Campos, Darym Júnior Ferrari de
Cavalcante, André Luís Brasil
Carvalho, José Camapum de
dc.subject.por.fl_str_mv Big data
Engenharia de fundações
Estacaria (Engenharia civil)
topic Big data
Engenharia de fundações
Estacaria (Engenharia civil)
description Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should understand how to take advantage of ordinary execution procedures to obtain the parameters of interest. Sensors are key components in engineering big data frameworks, as they provide a large number of valuable measured data. In particular, the building process (excavation and concreting) of continuous flight auger piles (CFAPs) can be fully monitored by collecting data from sensors in the drilling machine. This makes this type of pile an ideal candidate to utilize a big data methodology to indirectly obtain some constitutive parameters of the soil being drilled. Thus, in the present paper, the drilling process of CFAPs is modeled by a new physical model which predicts the energy spending during the execution of this type of pile. This new model relies on other fundamental properties of the soils drilled, such as unit weight, cohesion and internal friction angle. In order to show the applicability of the big data methodological framework hereby developed, a case study was conducted. A work site in Brasília-DF, Brazil, was studied and the execution of three CFAPs was monitored. Soil surveys were carried out to identify the soil strata in the site and, therefore, to validate the estimates of Young’s moduli provided by the new formulas. The 95% confidence intervals of Young’s moduli obtained for silty clay, clayey silt and silt were, in MPa, [14.56, 19.11], [12.26, 16.88] and [19.65, 26.11], respectively. These intervals are consistent with literature reports for the following materials: stiff to very stiff clays with low-medium plasticity, medium silts with slight plasticity, and stiff to very stiff silts with low plasticity, respectively. These were the types of materials observed during the site surveys; therefore, the results obtained are consistent with literature reports as well as with field surveys. This new framework may be useful to provide real-time estimates of the drilled soil’s parameters, as well as updating CFAPs designs during their execution. This way, sustainable designs can be achieved, where substrata materials are better characterized, avoiding over-designed structures.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-16T14:15:54Z
2023-10-16T14:15:54Z
2023-03-31
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 OZELIM, Luan Carlos de Sena Monteiro et al. An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles. Axioms, v. 12, n. 4, 340, 2023. DOI: https://doi.org/10.3390/axioms12040340. Disponível em: https://www.mdpi.com/2075-1680/12/4/340. Acesso em: 16 out. 2023.
http://repositorio2.unb.br/jspui/handle/10482/46679
https://doi.org/10.3390/axioms12040340
https://orcid.org/0000-0002-2581-0486
https://orcid.org/0000-0002-7128-3752
https://orcid.org/0000-0003-4980-1450
https://orcid.org/0000-0003-4155-4694
identifier_str_mv OZELIM, Luan Carlos de Sena Monteiro et al. An energy-based big data framework to estimate the young’s moduli of the soils drilled during the execution of continuous flight auger piles. Axioms, v. 12, n. 4, 340, 2023. DOI: https://doi.org/10.3390/axioms12040340. Disponível em: https://www.mdpi.com/2075-1680/12/4/340. Acesso em: 16 out. 2023.
url http://repositorio2.unb.br/jspui/handle/10482/46679
https://doi.org/10.3390/axioms12040340
https://orcid.org/0000-0002-2581-0486
https://orcid.org/0000-0002-7128-3752
https://orcid.org/0000-0003-4980-1450
https://orcid.org/0000-0003-4155-4694
dc.language.iso.fl_str_mv eng
language eng
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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Institucional da UnB
instname:Universidade de Brasília (UnB)
instacron:UNB
instname_str Universidade de Brasília (UnB)
instacron_str UNB
institution UNB
reponame_str Repositório Institucional da UnB
collection Repositório Institucional da UnB
repository.name.fl_str_mv Repositório Institucional da UnB - Universidade de Brasília (UnB)
repository.mail.fl_str_mv repositorio@unb.br
_version_ 1810580706628206592