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Record W4220883669 · doi:10.18280/rcma.320105

Moisture and Dry Density Influence on Compacted Clay and Clay-Sand Mixtures

2022· article· en· W4220883669 on OpenAlexvenueno aff
Bahia Louafi, Muawia Dafalla

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsExpansive claySwellSwellingWater contentBentoniteGeotechnical engineeringClay mineralsAtterberg limitsBulk densityClay soilGrain sizeSoil waterMaterials scienceGeologySoil scienceMineralogyComposite material

Abstract

fetched live from OpenAlex

The influence of initial moisture content and dry density on swelling clays was extensively investigated in the literature. In order to explore the relationships that exist between the swelling parameters of clay soils and the water content, initial dry density, and sand content, a series of experimental tests were conducted on pure clay and clay treated with 20% sand. The swelling pressure and the swell potential were also tested for sand-clay mixtures. The results showed a significant influence of the initial water content on the swell potential of bentonite and bentonite sand mixture. The increase in the dry density was found to boost the swelling pressure and swell potential. The rate of increase is related to sand content. For higher sand content, it was found that grain-to-grain contact reduces the compressibility of the mixture and thus causes the general swell trend to alter. The amount of clay particles within the mixture is an important factor affecting the swelling. The results demonstrated the effectiveness of the sand in the reduction of swelling parameters. This study provides a control measure for expansive soil materials using sand addition to achieve better soil for construction purposes.

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 score0.177
Threshold uncertainty score0.898

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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