Physical model studies and design guidelines for a new type of composite dynamic revetment
Bibliographic record
Abstract
A pebble or cobble beach, also known as a dynamic revetment, can provide a softer shore edge while also providing the resiliency necessary to prevent shoreline erosion. Research on dynamic revetments is very limited, especially when looking at design-ing for different sizes of cobble, and thus reliance on numerical solutions can lead to gaps in the expected performance of the design. For a proposed cobble beach on Lake Michigan’s Chicago shoreline, large-scale physical model tests were conducted to evaluate how the physical model would perform compared to the numerical models. These tests utilized a wave flume to run six alternative cross-section configurations. Two cross-sections evaluated small cobbles of uniform size, two configurations added a partially submerged detached breakwater in front of a uniform cobble beach, and the last two configurations looked at how a combination of small and large cobble behaved. Tests on the uniform configurations showed there was a feedback mecha-nism between the surging wave-breaker type and beach profile-shape deformation, resulting in the formation of a scour step at the toe and a large crest. Adding a de-tached breakwater provided no changes in the wave breaker causing scour step and crest formation. The composite dynamic revetment profile resisted formation of the scour step by reinforcing the toe and changing the wave breaker type to a plunging wave that breaks further offshore. This change disrupts the feedback mechanism that leads to the typical dynamic revetment profile. Simple mathematical relationships were derived to aid design of a new type of composite dynamic revetment offering improved coastal performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".