Assessment of rock mass erosion in unlined spillways using developed vulnerability and fragility functions
Bibliographic record
Abstract
Hydraulic power can lead to the erosion of rock and cause dams to be at risk of failure. Methods exist to predict the degree of erosion for rock masses in spillways; however, these deterministic approaches are unable to consider the uncertainties of rock mass parameters. We develop a methodology that determines the vulnerability of a rock mass to hydrological erosion, and this approach takes into consideration the uncertainties related to the parameters of the rock mass at the study site. Monte Carlo simulation is used to create a dataset for each class of rock for then developing fragility and vulnerability curves. The effects of each geomechanical parameter on erosion level can be determined by applying this methodology to individual spillway sites. As a result, sensitivity analysis shows that the discontinuity orientation factor is a critical parameter for explaining the erosion of a rock mass; increasing this parameter decreases the vulnerability of the rock mass to erosion. Our approach has an advantage over deterministic methods as the uncertainties of rock mass parameters have been considered. The error associated with the predicted erosion level via our probability-based approach is significantly less than the error obtained via deterministic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".