Estimating rheological properties of liquefied tailings for dam break simulation using site-specific parameters and laboratory testing
Bibliographic record
Abstract
Tailings dam failures, claiming human lives and causing catastrophic environmental impact, are unfortunately still frequently reported around the world. The tailings dam break simulation using site-specific parameters has now become an essential and critical part of the design, operation and closure cycle of every tailings storage facility (TSF), and is a requirement in many guidelines such as ANCOLD (2012), CDA (2021) and ICMM (2020). Urging from the mining industry and regulatory authorities for the development of better and more comprehensive simulating techniques that can accurately predict the flow behaviour of liquefied tailings in the hypothetical scenario of a tailings dam breach has significantly increased in recent years. The tailings deposited in a TSF often form a density profile with depth as the tailings consolidate and gain strength. This process increases the solids concentration and shear strength of the tailings within the TSF to a range that often makes direct measurement of the rheological properties of samples from the site using conventional bob and cup rotary viscometry impractical. Consequently, this imposes a challenge for obtaining reliable results from the tailings dam break simulation and needs to be overcome. A methodology is proposed in this paper for estimating the rheological properties of liquefied tailings at high solids concentrations when the direct measurement technique is impractical. The method is based on combining site-specific parameters such as the in situ dry density with laboratory-measured parameters such as the residual shear strength of the tailings after failure, and the bob and cup rotary viscometry data at lower solids concentrations. The method can be applied to establish a comprehensive understanding of the rheological behaviour of liquefied tailings at the wide range of solids concentrations required for dynamic tailings dam break modelling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".