Conditioning clayey soils with a dispersant agent for Deep Soil Mixing application: laboratory experiments and artificial neural network interpretation.
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| Title: | Conditioning clayey soils with a dispersant agent for Deep Soil Mixing application: laboratory experiments and artificial neural network interpretation. |
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| Authors: | Salvatore, Erminio1 (AUTHOR), Modoni, Giuseppe1 (AUTHOR), Spagnoli, Giovanni2 (AUTHOR) giovanni.spagnoli@mbcc-group.com, Arciero, Michela1 (AUTHOR), Mascolo, Maria Cristina1 (AUTHOR), Ochmański, Maciej3 (AUTHOR) |
| Source: | Acta Geotechnica. Nov2022, Vol. 17 Issue 11, p5073-5087. 15p. |
| Subjects: | Clay soils, Artificial neural networks, Kaolin, Soils, Soil particles, Strength of materials |
| Abstract: | Plasticity of clays makes Deep Soil Mixing (DSM) problematic due to the tendency of the material to congest the rotating blades, reduce mixing efficiency and remain clustered in lumps which affect the mechanical behavior of the cemented soil. The paper investigates this problem systematically with a comprehensive experimental campaign that shows the efficacy of a clay dispersant in scattering soil particles and making soil more workable. The performed laboratory investigation combines consistency, micro-structural, vane, rheological and uniaxial compression tests on two reference soils, a kaolin and a bentonite, treated with various proportions of cement, water and chemical additive to quantify the effects on workability, homogeneity and strength of the material. The variety of investigated conditions enables to understand the principles of chemical modification and infer a quantitative dependency of viscosity and uniaxial compression strength on the material composition. Observation is interpreted for the sake of generality with artificial neural networks, merging the role of the different components into a novel definition of the soil consistency index, accounting for the presence of the additive. The inferred empirical relations, expressed with charts, are proposed to optimize soil conditioning for DSM. [ABSTRACT FROM AUTHOR] |
| Copyright of Acta Geotechnica is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 159838576 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Conditioning clayey soils with a dispersant agent for Deep Soil Mixing application: laboratory experiments and artificial neural network interpretation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Salvatore%2C+Erminio%22">Salvatore, Erminio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Modoni%2C+Giuseppe%22">Modoni, Giuseppe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spagnoli%2C+Giovanni%22">Spagnoli, Giovanni</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> giovanni.spagnoli@mbcc-group.com</i><br /><searchLink fieldCode="AR" term="%22Arciero%2C+Michela%22">Arciero, Michela</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mascolo%2C+Maria+Cristina%22">Mascolo, Maria Cristina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ochmański%2C+Maciej%22">Ochmański, Maciej</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Acta+Geotechnica%22">Acta Geotechnica</searchLink>. Nov2022, Vol. 17 Issue 11, p5073-5087. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Clay+soils%22">Clay soils</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Kaolin%22">Kaolin</searchLink><br /><searchLink fieldCode="DE" term="%22Soils%22">Soils</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+particles%22">Soil particles</searchLink><br /><searchLink fieldCode="DE" term="%22Strength+of+materials%22">Strength of materials</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Plasticity of clays makes Deep Soil Mixing (DSM) problematic due to the tendency of the material to congest the rotating blades, reduce mixing efficiency and remain clustered in lumps which affect the mechanical behavior of the cemented soil. The paper investigates this problem systematically with a comprehensive experimental campaign that shows the efficacy of a clay dispersant in scattering soil particles and making soil more workable. The performed laboratory investigation combines consistency, micro-structural, vane, rheological and uniaxial compression tests on two reference soils, a kaolin and a bentonite, treated with various proportions of cement, water and chemical additive to quantify the effects on workability, homogeneity and strength of the material. The variety of investigated conditions enables to understand the principles of chemical modification and infer a quantitative dependency of viscosity and uniaxial compression strength on the material composition. Observation is interpreted for the sake of generality with artificial neural networks, merging the role of the different components into a novel definition of the soil consistency index, accounting for the presence of the additive. The inferred empirical relations, expressed with charts, are proposed to optimize soil conditioning for DSM. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Acta Geotechnica is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=159838576 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11440-022-01505-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 5073 Subjects: – SubjectFull: Clay soils Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Kaolin Type: general – SubjectFull: Soils Type: general – SubjectFull: Soil particles Type: general – SubjectFull: Strength of materials Type: general Titles: – TitleFull: Conditioning clayey soils with a dispersant agent for Deep Soil Mixing application: laboratory experiments and artificial neural network interpretation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Salvatore, Erminio – PersonEntity: Name: NameFull: Modoni, Giuseppe – PersonEntity: Name: NameFull: Spagnoli, Giovanni – PersonEntity: Name: NameFull: Arciero, Michela – PersonEntity: Name: NameFull: Mascolo, Maria Cristina – PersonEntity: Name: NameFull: Ochmański, Maciej IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 18611125 Numbering: – Type: volume Value: 17 – Type: issue Value: 11 Titles: – TitleFull: Acta Geotechnica Type: main |
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