A probabilistic approach to levee reliability based on sliding, backward erosion and overflowing mechanisms: Application to an inspired Canadian case study
Bibliographic record
Abstract
Abstract Improving protection against fluvial floods requires a better estimation of levee failure. We developed an assessment method of levee failure probabilities for sliding, backward erosion, and overflowing each represented by fragility curves. We tested two approaches to aggregate those fragility curves into a global fragility curve respectively using: an enveloping curve and Monte‐Carlo simulations. We implemented this approach to earthen levee reliability for several flood return periods to the Bow River in Calgary, Canada. We used limit equilibrium method to estimate the safety factor of the levee segment and Monte‐Carlo simulations to estimate sliding probabilities. We used Terzaghi's critical hydraulic gradient to estimate backward erosion failure probabilities. The estimation of overflowing probabilities required expert judgment. We discussed how the choice of the hydraulic gradient area and the consideration of a steady state or transient model impact backward erosion failure probabilities. The results showed for our study case that, even though the transient model is a closer representation of reality, the levee saturation parameter has little impact on hydraulic gradient values, by extension, on sliding and backward erosion failure probabilities. The Monte‐Carlo aggregated fragility curve is more realistic than the envelop curve of the failure mechanisms for an equivalent computation time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".