Characterization and Structural Improvement of Chott-El-Hodna Clay: A Study on Treatments Through Hydraulic Binder
Bibliographic record
Abstract
This study examines the characterization of Chott-El-Hodna clay and the enhancement of its properties using hydraulic binders, specifically cement and lime.Soil samples from Ain El Khadra in the Chott-El-Hodna basin were treated with varying concentrations of cement and lime (2-12% by weight) to evaluate their effects on the soil's mechanical and physical properties.Employing standardized geotechnical tests, including Proctor compaction, California Bearing Ratio (CBR), unconfined compression, and ultrasonic velocity measurements, this research assesses each binder's role in reducing soil plasticity, increasing compaction, and improving load-bearing capacity.Results indicate that both cement and lime contribute to improved soil stability, with lime showing superior performance in load-bearing capacity at higher dosages, while cement provides consistent compaction and strength benefits across all dosages.X-ray diffraction and fluorescence analyses further highlight the chemical and mineral stability of the treated soils, with quartz and calcite enhancing mechanical resilience and pH buffering.These findings suggest that lime and cement treatments can significantly improve the durability of infrastructure in saline soil regions, offering targeted stabilization solutions to optimize foundational integrity in arid and semi-arid environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".