Mix optimization for expansive soil stabilized with a novel waste material-based geopolymerization approach
Bibliographic record
Abstract
Extensive areal distribution of expansive soil is a timely concern in engineering challenges. Geopolymers outperform conventional cement/lime treatment in expansive soil stabilization due to their superior mechanical and durability performance. However, the excessive cost and carbon footprint of commercial alkaline activators hinder geopolymer's widespread application. This study aims to derive a cost-effective, carbon-conscious mix to stabilize expansive soil using waste-based geopolymerization. Class F fly ash was activated via a novel solution of rice husk ash (RHA)-derived silicate and NaOH. Three factors (NaOH/RHA, NaOH molarity, mixing duration) were considered using the Taguchi method and utility concept for mix optimization, while further investigations were tailored to explore the effects of curing temperature (room temperature, 30 °C, and 40 °C) and the curing period (7, 14, and 28 days) on the strength development of treated soil. The results indicate that NaOH/RHA = 0.6, NaOH molarity = 3 mol/L, and a mixing duration of 40 min with curing temperatures of around 30 °C are ideal for maximizing the strength cost-effectively while significantly reducing the swell pressure (up to 28%). The shift from commercial Na2SiO3 to RHA-silicate is 89% cheaper and reduces the carbon footprint by 70%. The study benefits sustainable ground stabilization and efficient waste management.
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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".