Centrifuge modelling of drawdown seepage in tailings storage facilities
Bibliographic record
Abstract
Uncertainties surrounding seepage behaviour are a key issue raised in tailings storage facility (TSF) operation. Poor seepage management can result in negative environmental impacts, costly remediation or even embankment failure and, in the context of mine closure, long term liabilities and/or legacy site issues. In particular, recovery pumping rates must be maintained for sufficient time to capture seepage both during operation and after closure during reservoir drawdown. Seepage analyses for TSF design commonly assume isotropic or, at best, anisotropic homogeneous material properties. However, layering during deposition, consolidation and swelling on drying and wetting create a seepage environment far more complex than these assumptions suggest. Improved modelling is required to increase analysis confidence. Centrifuge modelling allows geotechnical phenomena to be investigated using scale models under representative stress conditions. However, precious few examples exist for seepage modelling using this technique. This paper briefly discusses modelling equipment development for use with The University of Western Australia (UWA) beam geotechnical centrifuge. Results for seepage during reservoir drawdown, simulating facility closure, are then presented for a layered, heterogeneous embankment model, as compared to predictions made by commercial analysis software. Findings are used to comment on the implication of simplifying analysis assumptions on drawdown time and flowrate calculations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".