Role of calcined-clay-based geopolymers for effective stabilization of expansive soils
Bibliographic record
Abstract
Effective stabilization of expansive soils remains a challenge for transportation infrastructure. In line with the United Nations’ sustainability goals, most research on eco-friendly stabilizers is still in its early stages, focusing primarily on laboratory evaluations. This study aims to evaluate the efficacy of alkali-activated calcined clay-based geopolymers (CCBGPs) and investigate the most effective minerals contributing to its stabilization of expansive soils. Four locally available clays were used to synthesize CCBGPs, and based on unconfined compressive strength, two of them were selected for further stabilization of expansive soils. The treated soils underwent comprehensive engineering, microstructural, and mineralogical analyses. A machine learning (ML)-based regression model was developed and tested to examine the effects of CCBGP and soil mineralogy on engineering performance of the treated soils. Engineering tests on treated specimens showed improved performance with increasing CCBGP dosage and curing periods, while microstructural and mineralogical analyses revealed physical and chemical interactions with soil particles that enhanced engineering properties. The ML model identified kaolinite as the most influential factor, which enhanced geopolymerization by forming more binder gel, and improved engineering properties. Overall, the research indicated that selection of kaolinite-rich, locally available CCBGPs could serve as sustainable source for improving expansive soils in transportation infrastructure.
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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) | 0.000 | 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".