Numerical Investigation on Tailing Dams Stability: a Preliminary, Parametric Analysis of some Key Factors
Bibliographic record
Abstract
Tailing wastes are by-products of mining industry and are generally mixtures of rock, sand, fine-grained solid material and in some cases relevant quantities of heavy metals and water remaining after the mineral values have been extracted from the patent ore.In recent years the amount of tailings has significantly increased to meet the growing demand for metals and minerals.Huge amounts of tailing wastes are produced and discharged inside storage facilities (TSF), also known as tailing dams.Owing to their complexity and high rate of collapses with relevant loss of human lives, economic and environmental damages, a detailed knowledge of the hydromechanical properties of tailings is essential to develop a reliable stability analysis both for new and existing structures.This research provides a preliminary parametric study aimed at investigating the impact of some fundamental design aspects.The influence of the adopted numerical method, raising techniques, distance of decant pond, hydraulic conditions, geometry of drainage systems and uncertainty of geotechnical properties on stability of an embankment have been evaluated for a simple case, providing some fundamental concepts to be considered when designing new tailing dams or performing stability analysis on existing ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".