Tailings Dams Numerical Models: A Review
Bibliographic record
Abstract
A significant number of tailings dam failures have occurred around the world in the last few decades resulting in fatalities, damage to infrastructure and environmental harm.Among these, many have been caused by the liquefaction phenomenon that can suddenly transform an earthen dam into a liquid river of mud.To date, many general aspects related to tailings dam failures and tailing management have been dealt with in the literature.However, the materials used to build the dams, mainly consisting of underconsolidated silts, are still poorly studied and adequate modeling of their behavior is still an open challenge.This paper presents the state of existing knowledge on this latter topic.The problem related to the storage of mining residues in tailings dams is first described.For this purpose, fifteen scientific articles, in which numerical modeling is carried out on this type of structures, are analyzed.Aspects relating to the type of structure investigated and connected to numerical modeling such as software and constitutive models used are reported and commented.A summary of the main geotechnical parameters used in the modeling is presented and analyzed.Finally, the most salient aspects of the results obtained from the various analysis are exposed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".