Effect of Upstream Dam Geometry on Peak Discharge During Overtopping Breach in Noncohesive Homogeneous Embankment Dams; Implications for Tailings Dams
Bibliographic record
Abstract
Abstract Tailings dams differ from conventional water‐retaining dams in design, materials retained behind the dam and used in construction of the dam, and the inclination of the upstream face of the dam. In this paper, we isolate the effect of upstream slope angle on behavior during overtopping breach. Six 1 m high homogeneous fine sand dams were constructed with upstream slope angles varying between 10.0° and 30.0° and brought to failure by V notch overtopping. Slope angle was observed to define the height of flow over the erosional breach crest hydraulic control structure that forms in the upstream face of the dam, with higher peak outflow corresponding to steeper upstream slope angles. A semiellipse was observed to be an excellent approximation of the geometry of the breach throughout the rising limb of the hydrograph to peak outflow. This observation permitted the use of the Ramanujan approximation for perimeter of an ellipse to define a mathematical relationship between breach width and arc length. A simplified method to predict the rising limb of the outflow hydrograph was then proposed based on the assumption of linear growth of breach width coupled with a semielliptical breach geometry. These findings show that hazard analysis for overtopping failure should consider the effect of upstream slope angle on peak outflow.
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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) | 0.000 | 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".