Modélisation physique de l’érosion-affouillement au pied d’un ouvrage vertical de protection côtière.
Bibliographic record
Abstract
Les murs côtiers au Québec sont soumis à un stress important lors des ondes de tempêtes. Ces évènements extrêmes érodent la plage au pied des ouvrages, formant une fosse d’affouillement et menaçant la stabilité des ouvrages. Une étude a été menée afin d’améliorer les connaissances sur les processus de transports sédimentaires sous l’effet des vagues. Ces travaux présentent une étude rigoureuse de modélisation physique, avec le développement d’un montage expérimental, d’un protocole, puis de la réalisation de différentes campagnes expérimentales. Le suivi de la surface libre, des vitesses à proximité du mur et de l’évolution morphodynamique de la plage est effectué avec une technologie à haute résolution. Le suivi des vitesses se fait par Planar Laser Induced Fluorescence (PLIF) et une technologie acoustique est utilisée pour mesurer la bathymétrie. Sept paramètres d’intérêt ont été retenus ; les conditions hydrodynamiques (profondeur d’eau, période, hauteur de vague), sédimentaires (diamètre, pente) et les caractéristiques de l’ouvrage (position, géométrie). Deux processus distincts ont été observés, soit une évolution rapide, puis une stabilisation à une profondeur inférieure à la hauteur de vague et une évolution lente avec une profondeur d’affouillement supérieure à la hauteur de vague. Coastal walls in Quebec are subject to significant stress during storm surges. These extreme events erode the beach at the foot of the structures, forming a scour hole and threatening the stability of the structures. A study was conducted to improve the knowledge of sediment transport processes under waves. This work presents a rigorous study of physical modelling, with the development of an experimental set-up, a protocol, and the realization of various experimental campaigns. The monitoring of the free surface, velocities near the wall and the morphodynamic evolution of the beach is carried out with a high-resolution technology. The monitoring of velocities is done by PLIF and an acoustic technology is used to measure the bathymetry. Seven parameters of interest were selected: hydrodynamic conditions (water depth, period, wave heights), sedimentary conditions (diameter, slope) and the characteristics of the structure (position, geometry). Two distinct processes were observed: a rapid evolution, then stabilization at a depth lower than the wave height and a slow evolution with a scouring depth higher than the wave height.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".