Shear wave velocity and small-strain stiffness across a broad range of states in iron ore tailings
Bibliographic record
Abstract
Recent failures of iron ore tailings storage facilities highlight gaps in understanding the behaviour of these materials. Stiffness is an important tool in the assessment of static liquefaction triggering and the monitoring of potential dam degradation that may result. This study evaluated the variation of the elastic stiffness across a wide range of void ratios and fines content and proposes a new model for the variation of shear modulus with mean effective stress. As commonly found, the shear wave velocity is more stress dependent for tailings in a loose state than in a dense state, but compared to “natural” soils, the shear modulus is significantly affected by the heavy (high specific gravity) fines content. These outcomes suggest that the addition of heavy fines (specific gravity > 3.5 vs. ∼2.7 of most natural soils) can increase the shear modulus without increasing the shear wave velocity. From these results, a boundary anchored to the state parameter that separates contractive and dilatative behaviour can be found using Vs measurements. This provides an important monitoring tool for iron-ore tailings dams in light of two recent failures (Fundão and Feijão) that displayed minimal prior warning.
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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) | 0.000 | 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 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".