Aspects aléatoires de l'érosion d'une digue : simulations de la brèche par des algorithmes génétiques
Bibliographic record
Abstract
Attempts to forecast the final form of a dam-break suffer from various uncertainty sources such as field data (topography, hydraulic and geotechnical parameters), and modeling assumptions. So the result seems to be at least partially chaotic and difficult to match with the objectives of the dam-safety engineers in which the goal is to model dam-break flow evolution in time. On the other hand, the principle of minimal energy dissipation rate makes it possible to predict the shape towards which the geometry of the breach will evolve to under stationary hydraulic conditions. In this article, one uses energy minimization by a genetic algorithm to integrate at the same time the two concepts of random result and convergence towards an equilibrium state. They are used to simulate the submersion failure of a clay dam, and the results agree reasonably with the field data. This approach makes it possible to obtain as realistic breach shapes as those obtained by actual models, and to introduce a random component in the process of dam-breach formation, which remains, however, guided by a physical principle.Key words: erosion, dam breach, genetic algorithms, sediment transport.
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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 | 0.000 | 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".