Effect of the presence of a tailings dam beach on breach outflow and erosion during overtopping failure
Bibliographic record
Abstract
Dam breach analyses for tailings dams currently rely heavily on relationships and methods derived for water retaining dams, despite significant differences in design, construction, dam materials, and geometry; particularly, the upstream face of the dam. Conventional tailings slurry deposition from the dam crest typically forms low angle upstream beaches (1–2 % inclination) within the impoundment. In this paper, we isolate the effect of tailings dam beach geometry at the time of overtopping on breach characteristics using physical and numerical modeling. Five 1 m high homogeneous fine sand dams with beach heights of 0.5 to 0.9 m and a beach slope of 5 % were brought to failure by v-notch overtopping. The laboratory data revealed that a threshold beach height existed above which the peak discharge was progressively limited by the geometry of the reservoir. Numerical simulations, performed in XBeach, captured this effect in the outflow hydrographs, with differences between physical and numerical model peak outflow generally within 25 %. Another key model parameter in tailings dam breach analysis is the volume of tailings solids lost through erosion during breach. Comparison of terrestrial laser scanning elevation profiles, cut through the centreline of the physical model, with XBeach simulations indicate XBeach can replicate the bulk characteristics of erosion when a tailings-style beach is present. These findings show that hazard analysis for overtopping failure in tailings dams should consider the effect of tailings dam beach geometry on the outflow hydrograph, and forms a growing case of evidence to support the use of XBeach for simulation of dam breach. • Presence of a tailings beach can affect outflow hydrograph during overtopping. • Observations show a threshold beach elevation exists for influencing hydrograph. • XBeach simulations successfully captured outflow hydrograph and volume.
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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 | 0.000 | 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".