Compressive Strength Characteristics of Cemented Tailings Backfill with Alkali-Activated Slag
Bibliographic record
Abstract
With the use of glauberite mineral (GM) and sodium hydroxide (SH) alkaline catalysts to stimulate slag powder’s internal cementation activity and incorporate the two fine-grained solid wastes, such as quicklime (Q) and desulfurized ash (DA), a new cementitious material suitable for mine tailings was developed to replace traditional ordinary Portland cement (OPC) for reducing cement-related costs. A series of uniaxial compressive strength (UCS) tests were carried out on cemented tailings backfill (CTB) samples containing different activators. The results showed that (1) the highest UCS values of 14-day and 28-day cured CTB samples were 1.259 MPa and 2.429 MPa, respectively, and the effect of different activator types was in the order of SH > GM > DA > Q and SH > GM > Q > DA; (2) the relationship between UCS and activator dosages followed the function y = ax3 − bx2 + cx − d. Compared with the OPC 32.5 R cemented samples, the minimum strength growth factor was 1.45, and the maximum reached 2.03; (3) the optimal proportion of DA slag formula was 4.5% or 5.0% Q, 19% DA, 2.5% GM, and 0.7% SH. The aforesaid new cementitious materials met the mine’s UCS requirements with a relatively low cost (17.04–17.20 €/ton) and solved the stacking problem of solid wastes on the surface well. Ultimately, this study provides a useful reference for the development of mineral binders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".