Experimental Investigation of Flood Energy Dissipation through Embankment Followed by Emergent Vegetation
Bibliographic record
Abstract
The combination of hard (artificial) and soft (natural) solutions i.e., composite defense systems against flooding and tsunami opens a new window for engineering innovation for researchers nowadays. In this study, the experimental investigation of flood energy dissipation phenomena through composite defense systems comprising of embankment and rigid vegetation models in an open channel flume, is conducted. The flow regime through the composite defense system is classified in two main types, which are further subdivided in two sub-categories. Various combinations of embankment and vegetation and spacing between embankment and vegetation are analyzed. Against the selected range of initial Froude numbers, three different sizes of embankment models, three spacings between the embankment and vegetation (Ldv) and vegetated corridors of two different porosities (PR), are tested to examine the effect of these three parameters on the characteristics of the generated hydraulic jumps and flood energy dissipation within the defense system. It is found that embankment size and vegetation porosity have a greater impact on flood energy dissipation while the selected range of Ldv is less effective. Amongst the assessed composite flood defense systems, the maximum energy dissipation (55%) is observed for the combination of maximum embankment height and vegetation porosity (93%). For fixed combinations of embankment size and Ldv, the maximum increase of energy dissipation (18%) is found for decreasing vegetation porosity from 97% to 93%.
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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 | 0.000 |
| 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) | 0.001 | 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".