Clay Soil Stabilization Using Sugarcane Ash and Lime
Bibliographic record
Abstract
This study investigates the effectiveness of sugarcane bagasse ash (SBA) and lime as chemical stabilizers for clay soil subbase improvement.The research investigates clay soil from Taxila Pakistan which received stabilization treatment by SBA and lime along with their combination at different mixing ratios.A series of geotechnical tests, including Atterberg limits, compaction tests, and California Bearing Ratio (CBR) tests, were conducted on both untreated and stabilized soil samples.SBA and lime were used in concentrations of 2.5%, 5%, and 7.5% by dry soil weight, while their mixtures were applied in ratios of 1:1, 2:1, 3:1, 1:2, and 1:3 at 5%, 7.5%, and 10% of dry soil weight.The results indicate that soil mixed with 7.5% SBA exhibited a 28% increase in the liquid limit, while the combination of 2.5% lime and 7.5% SBA resulted in a 40% increase in the plastic limit.The plasticity index improved by 42% with 7.5% SBA, and a mixture of 2.5% lime and 2.5% SBA significantly reduced soil plasticity, classifying it as low-plasticity soil.Moreover, the highest improvement (69%) in the CBR value was observed at 2.5% SBA and 5% lime mixture, which indicates the significant enhancement of the strength enhancements of the pavement soil.The cost analysis of the treated pavement shows that this method serves as an environmentally friendly practice that lowers roadway costs while prolonging service span and solving disposal issues through waste material conversion to SBA.The research findings confirm SBA alongside lime as an affordable sustainable stabilizer suitable for road construction projects that require clay soil improvement.
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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".