Effect of Extreme Heating on the Mineralogy and Microstructure of Expansive Soil
Bibliographic record
Abstract
Expansive soils pose significant challenges in geotechnical engineering due to their pronounced volume changes in response to moisture fluctuations, leading to substantial economic losses annually.This study investigates the effects of extreme thermal treatment on the mineralogical and microstructural properties of expansive soils.Soil samples collected from Al Ghat, Saudi Arabia, were subjected to a controlled heating regime of 600°C for two hours.Advanced analytical techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), particle size distribution (PSD) analysis, and energy-dispersive X-ray spectroscopy (EDS), were employed to evaluate changes in mineralogical composition, microstructure, and elemental composition.Results revealed significant alterations in the soil's properties, including particle aggregation, reduced fine particle content, and the formation of larger, more stable aggregates.XRD analysis indicated the disappearance of kaolinite peaks, suggesting dehydroxylation and the formation of metakaolin, while EDS analysis showed a reduction in oxygen content and enrichment of certain cations.These findings align with previous research, demonstrating that thermal treatment effectively reduces the expansive characteristics of soils by altering their mineralogical and microstructural properties.The study underscores the potential of thermal stabilization as a viable method for mitigating the adverse effects of expansive soils in geotechnical engineering applications.
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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".