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Record W3049343908 · doi:10.1139/cgj-2019-0855

Increasing density and cement content in stabilization of expansive soils: Conflicting or complementary procedures for reducing swelling?

2020· article· en· W3049343908 on OpenAlexvenueno aff
Nilo César Consoli, Mariana Tonini de Araújo, Suéllen Tonatto Ferrazzo, Vinícius de Lima Rodrigues, Cecília Gravina da Rocha

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoMinisterio de Economía y CompetitividadCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsExpansive clayPortland cementCementSwellingGeotechnical engineeringWater contentSoil waterCompactionBentoniteMaterials sciencePorosityGeologyEnvironmental scienceSoil scienceComposite material

Abstract

fetched live from OpenAlex

The present study makes three contributions to the literature of expansive soils: (i) it proposes equations to predict soil swelling based on dry density and cement content, (ii) it checks the developed general equation by predicting the swelling of different expansive soils from the literature, and (iii) it designs experiments that investigate factors that have a significant influence on swelling. An experimental programme was carried out to analyse the expansion of bentonite–kaolin–cement blends. Different proportions of bentonite–kaolin, cement content, dry density, and moisture content were evaluated. A unique relation of the cement/porosity index was obtained for cement-stabilized expansive soils’ swelling; this index has been used before to portray strength, stiffness, and loss of mass of stabilized soils and is now shown to be applicable to describe swelling of expansive soils treated with Portland cement. In the present research, cement content and dry density are seen as conflicting parameters regarding the swelling of expansive soils, because increasing the amount of Portland cement reduces swelling and increasing the density (through compaction) causes higher expansion. A general swelling model is proposed and successfully checked with data from the literature; it is able to predict the swelling of expansive soils with different densities, expansive mineral, moisture content, and cement content.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.251
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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