Coal-derived char for durability improvement of cement stabilized soil under freeze–thaw, wet–dry, and sulfate attack
Bibliographic record
Abstract
Cement soil stabilization is widely used in civil engineering to improve the performance of soils subjected to freeze–thaw (F–T), wet–dry (W–D), and sulfate attack (SA). Due to the negative impacts associated with manufacturing cement, the development of eco-friendly and sustainable additives is highly desirable. Coal-derived char is a cost-effective byproduct of the coal pyrolysis process. In this study, the influence of coal char on mineralogical, microstructural, physical, and mechanical properties of cement stabilized soils (with cement contents of 0%–20% and char contents of 0%–30%) subjected to F–T cycles, W–D cycles, and SA is investigated. Compared to cement stabilized soils, char-cement stabilized soils exhibit up to 60.8% fewer volume changes during F–T cycles and 31.6% fewer during W–D cycles. The compressive strength of char-cement stabilized soils with cement contents of 5%, 10%, and 20% are on average 7.9%, 17.6%, and 11.0%, respectively, higher than that of cement stabilized soil subjected to F–T cycles, W–D cycles, or SA. The inclusion of char promotes cement hydration and results in the formation of more amorphous hydration products that fill voids or cover soil minerals. The findings indicate the promising potential of coal char in enhancing soil performance under a range of challenging environmental conditions.
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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 | 0.001 |
| 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".