Tailings Dam Breach Outflow Modelling: A Review
Bibliographic record
Abstract
Abstract Tailings dam breach modelling studies have received considerable attention recently due to the rise in the number of tailings dam failures and catastrophic consequences caused by downstream flooding. Numerical models are useful tools in risk management for assisting urban planners in planning for the safe evacuation of the vulnerable communities located downstream in the so-called “shadow area” of such dams. Several challenges and uncertainties exist when conducting risk assessments of tailings dam failure. In this study, recent advances in modelling approaches for tailings dam breach analysis and downstream flood wave routing are summarized and critically reviewed. This study evaluates different mudflow modelling studies that involve single-phase, quasi-two-phase, and two-phase modelling approaches; dam breach outflow modelling; tailings rheological characterization; and application of geographic information system (GIS) and remote sensing to tailings dam breach analysis. Recommendations for further research are provided based on the findings. In addition, this study will help dam engineers and practitioners to maintain industry standards and include state-of-the-art practices in their work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".