Improvement of tailings impoundment seismic and post-seismic stability using densification and waste rock inclusions
Bibliographic record
Abstract
Tailings impoundments are often prone to failure due to various causes related to complex loading conditions. For instance, tailings impoundments can fail during or after a seismic event, mainly because of the liquefaction of loose, cohesionless tailings. To improve the seismic stability of impoundments, the density of the tailings can be increased or waste rock inclusions (WRI) can be added in the impoundment. This paper presents the results of numerical analyses of the impoundments loaded with a variety of ground motions with different frequencies, scaled at two different levels of intensity representative of two mining regions of Eastern Canada. The results show that the conventional impoundments are subjected to medium to high displacements at the end of shaking; these show a high probability of rupture during the earthquake or in the post-seismic phase. Tailings densification can decrease the volume of liquefied tailings and displacements during the seismic phase but does not significantly reduce the probability of post-seismic failure. WRI can slightly reduce the volume of liquefied tailings, depending on the intensity of the motions, but the reinforcement reduces significantly the lateral displacements; impoundments with WRI show even better performances during the post-seismic phase. The use of WRI is deemed a good approach to ensure the seismic stability of tailings impoundments and reduce the risk of seismically induced failure.
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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 | 0.000 | 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".