Evaluation of the stress history in a tailings dam raising stages: A study based on Finite Element Method (FEM) methodology
Bibliographic record
Abstract
Tailings dams are complex geotechnical structures that require a thorough analysis of their stability.The traditional method for assessing stability, the Limit Equilibrium Method (LEM), focuses on calculating the factor of safety (FS), but omits critical aspects such as the stress distribution along the regrowth process.The finite element method (FEM), based on the strength reduction technique (SSR), is an alternative that allows calculating the FS and understanding the real behavior of the slope, by analysing stresses along the different raises stage.In this study, the FEM method was used to simulate the Ancash tailings dam, Peru.The simulation allowed obtaining detailed information on the stress states to which the soil foundation is subjected at each stage of regrowth.The results obtained in terms of displacements and stresses provided a more accurate understanding of the failure mechanism to which the slope may be subjected.It was concluded that the FEM method demonstrated its superiority over the traditional LEM approach, as it provides a more complete and realistic appreciation of the behavior of the slope body at different stages of its development.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".