Effects of Sodium Nanoalginate and Lime on Swelling Properties of Expansive Soils
Bibliographic record
Abstract
The findings revealed that the addition of nanoalginate and lime had distinct effects on various soil properties. Specifically, the liquid limit (LL) and plastic limit (PL) decreased when sodium nanoalginate and lime were added, while the plasticity index (PI) and shrinkage limit (SL) increased. Furthermore, the soil classification was altered when sodium alginate and lime were introduced to the control soil. Regarding the standard Proctor test, it was observed that adding sodium nanoalginate increased the maximum dry density and reduced the optimal moisture content, whereas lime had the opposite effect by decreasing the maximum dry density and increasing the optimal moisture content. The free swelling and swelling pressure tests indicated that the incorporation of sodium nanoalginate and lime reduced both free swelling and swelling pressure. The most significant reduction was observed in the sample containing 7% sodium nanoalginate and 5% lime. Additionally, the study highlighted the influence of processing time, showing that an increase in the curing time led to a decrease in free swelling and swelling pressure in samples mixed with 3% sodium nanoalginate and lime. The XRD test showed that adding sodium nanoalginate reduced primary minerals, forming SAH, while lime reduced quartz and calcite, creating CSH. Overall, the results suggest that sodium nanoalginate can be a more environmentally friendly alternative to lime for soil stabilization projects.
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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 | 0.000 | 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".