Numerical analysis of strengthening by rockfill embankments on an upstream tailings dam
Bibliographic record
Abstract
The consolidation process could be slow in an upstream tailings dam; therefore, the stability can reduce due to an increase in excess pore pressures when the dam is raised. The safety of the dam can be enhanced by constructing rockfill berms on the downstream side. This paper presents a case study on the strengthening of an upstream tailings dam with rockfill berms. The finite element analyses were performed for modelling the staged construction of the dam and for optimizing the volume of the rockfill berms. The dam was raised in 11 stages; each stage consisting of a raising phase and a consolidation phase. The study shows that the slope stability of the dam reduced due to an increase of excess pore pressures during the raising phase. The stability of the dam was successfully improved by utilizing rockfill berms as supports on the downstream side. A technique has been presented to minimize the volume of the rockfill berms so that the required stability can be achieved at minimum cost. This paper shows that the finite element method can be a useful tool for modelling the consolidation behaviour of an upstream tailings dam and minimizing the volume of the rockfill berms that may be needed to maintain the stability of the dam during staged construction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".