Experimental Analysis of the Strength and Microstructural Effects of Rice Husk Ash and Bamboo Leaf Ash on Cement-treated Lateritic Soil
Bibliographic record
Abstract
The two most often utilized binders in soil stabilization projects are lime and Portland cement, but this process has a high rate of carbon emission.However, research on soil stability through the use of cleaner materials and environmentally friendly binders with a lower carbon footprint has garnered a lot of attention in recent years due to the significant carbon emissions in cement manufacturing.In this study, the strength and microstructural characteristics of lateritic soil stabilized with bamboo leaf ash (BLA) and rice husk ash (RHA) were examined.The soil sample underwent preliminary examinations and California bearing ratio tests as part of the testing process (CBR).The soil sample was then individually mixed with BLA and RHA at 2% intervals in proportions of 0-16%, as well as cement in variable proportions of 0-12% at 2% intervals.The blends were tested for CBR at each stage.The greatest values for unsoaked and soaked CBR at 8% cement +8% RHA were 92.3% and 70.2%, and 90% and 62.2%, respectively, at 8% cement +8% BLA for soaked and unsoaked CBR.SEM and X-ray diffraction (XRD) tests were performed on samples that had reached these ideal CBR levels.According to the results, new compounds were created and the microstructural layouts changed.Therefore, it may be inferred that the stabilization process involved pozzolanic and cement hydration reactions.
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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".