Triaxial testing response of compacted iron ore tailings considering a broad spectrum of confining pressures
Bibliographic record
Abstract
The filtered tailings disposal (dry stacking) up to 300 m high is an alternative to overcome the drawbacks related to the slurry tailings storage in large impoundments as it is safer and demands smaller portions of the existing landform. Even so, the understanding of the denser and dewatered material response over a broad range of confining pressures is essential to safely design tall dry stacking tailings facilities. Accordingly, the present research assesses the mechanical behavior of compacted iron ore tailings through triaxial tests. A series of compression and extension drained and undrained triaxial tests were conducted over a wide spectrum of confinements (σ́3 ranging from 75 to 8,000 kPa) to check possible particle breakage occurrence and effects. The influence of the initial density due to compaction was, as well, evaluated since the tests were performed using specimens molded at distinct dry unit weight values. The results were analyzed in the light of the critical state soil mechanics and have indicated the existence of a curvilinear critical state line in the ν: log ṕ plane. Small particle breakage has occurred and can be associated with reduction in surface roughness, breakage of asperities, and reduction in particle angularity. Moreover, a tendency for static liquefaction was observed amongst the loosest specimens sheared under the lowest confining levels.
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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 | 0.001 |
| 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".