Mechanical properties of sulfate saline soil stabilized by coal gangue-slag composite geopolymers
Bibliographic record
Abstract
Abstract Geopolymers, which are composed of solid waste, are eco-friendly binders characterized by rapid hardening and high strength. Using geopolymers to stabilize saline soils presents a sustainable solution for soil improvement. This study investigated two geopolymers, HG and SG, composed of a coal gangue-slag composite, to improve saline soils. The stabilized soils were prepared by blending geopolymers with saline soils in varying proportions of 10, 20, 30, and 40% by weight. The strength of the stabilized soils was evaluated based on the geopolymer dosage. Moreover, we examined the strength of the stabilized soils with curing time and elucidated the underlying mechanisms using microscopic analyses including XRD, FTIR, and SEM. This study revealed that: 1) the SG geopolymer outperformed the HG geopolymer in soil strength enhancement; 2) cohesion of the stabilized soil increased as the dosage and curing time increased, while the internal friction angle showed no clear trend; 3) the geopolymer stabilized soil mainly contained quartz, C-A-S-H, N-A-S-H, and C-N-A-S-H gels, and these gels were distributed on the soil particle surface and within the interstitial voids, improving the soil strength, and 4) in saline soil engineering, HG or SG geopolymer dosages should be above 30% and 20%, respectively.
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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".