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
Authors:	Ozelim, Luan Carlos de Sena Monteiro Campos, Darym Júnior Ferrari de Cavalcante, André Luís Brasil Carvalho, José Camapum de
metadata.dc.identifier.orcid:	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
metadata.dc.contributor.affiliation:	University of Brasilia, Department of Civil and Environmental Engineering
Assunto::	Big data Engenharia de fundações Estacaria (Engenharia civil)
Issue Date:	31-Mar-2023
Publisher:	MDPI
Citation:	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.
Abstract:	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.
metadata.dc.description.unidade:	Faculdade de Tecnologia (FT) Departamento de Engenharia Civil e Ambiental (FT ENC)
Licença::	Copyright: © 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/).
DOI:	https://doi.org/10.3390/axioms12040340
Appears in Collections:	Artigos publicados em periódicos e afins

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