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Record W7102397603 · doi:10.18280/mmep.120910

Influence of Saline Treatments on the Geomechanical Behavior of Clayey Soils

2025· article· en· W7102397603 on OpenAlexvenueno aff

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsExpansive claySwellingSoil waterClay soilAbsorption of waterSalt (chemistry)PotassiumClay minerals

Abstract

fetched live from OpenAlex

Expansive clay soils are made up of many minerals such as hydrated aluminum silicates, which have a fibrous, laminated structure and give the soil a high water absorption capacity. The volumetric change of such soils in the presence of water poses serious stability problems. Improving soil performance is an important process in the construction of geotechnical works on problem soils. There are several methods to solve these issues, among them stabilization by injection of chemical solutions into the soil. In this context, this study focuses on the injection of chemical solutions in a clay taken from the region of Didouche Mourad, in the district of Constantine in the north east of Algeria. In the first part, the physical and mechanical characterization of clay was conducted in a series of laboratory tests. The results obtained show that our clay exhibits high swelling potential (Swelling potential: 3.87%), very plastic (plasticity index: 38.55%) and moderately compressible. The second part presents the treatment of swelling clays by adding Potassium chloride (KCl) and sodium chloride (NaCl) at different concentrations of 0.5, 1 and 2 mol/l. The results show that salt addition markedly improves soil behavior, with swelling reduced by 25–72% at 0.5–1 mol/l and by up to 75% at 2 mol/l. Both salts were effective, with KCl slightly outperforming NaCl. This confirms that concentrated salt solutions are a promising method for stabilizing expansive clays in geotechnical applications.

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 score0.076
Threshold uncertainty score0.342

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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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