Iron ore tailings stabilization with alternative alkali-activated cement for dry stacking: mechanical and microstructural insights
Bibliographic record
Abstract
Upstream tailings dams are high-risk structures that have experienced several failures worldwide, particularly with iron ore tailings (IOT). In this study, new disposal methods/techniques, such as cement-stabilized dry stacking, are discussed that provide enhanced mechanical behavior while reducing failure risks. Alkali-activated materials are used as cementing agents due to their mechanical and environmental advantages compared to ordinary Portland cement. This study evaluates the mechanical and microstructural behavior of IOT stabilized with an alkali-activated cement (AAC) composed of two by-products from the IOT beneficiation process, metakaolin and sodium silicate, tested under plane strain conditions. Simple shear tests and microstructural analysis were performed. Mixtures of IOT were produced with 0%, 1%, 3%, and 5% AAC to examine the influence of such variables on strength and deformability parameters under undrained conditions. The mixtures with 3% and 5% AAC showed the greatest impact on the strength; however, the addition of 1% AAC was able to reduce positive pore-pressure generation. Cementitious bounds were evidenced by forming a sodium aluminosilicate hydrate gel. The studied AAC was effective in stabilizing IOT, even at small contents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".