Application of reliability techniques for the estimation of uncertainties in fluvial hydraulics simulationsThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.
Bibliographic record
Abstract
This work forms part of a general research effort into developing a methodology for reliability estimates of results obtained in numerical hydrodynamic computations. We consider the possibility of overtopping at the crest of a dyke of height h 0 positioned on a river with several uncertain parameters, such as discharge, Manning coefficient, bathymetry, etc. The estimation model is based either on the first-order reliability method (FORM), on the multi-form method, or on the importance sampling methods and is coupled with algorithms for the resolution of an adjoint optimization problem. Numerical tests are carried out on flows over a channel with a bump and on a river. The results obtained with our algorithms are compared with those obtained with the commercial software Nessus ® and with the Monte Carlo method. The proposed multi-form approach combined with a robust optimization algorithm provides reliable results within reasonable computation times.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".