Computational Intelligence on Hydrodynamic Performance Characteristics of Emerged Perforated Quarter Circle Breakwater
Bibliographic record
Abstract
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (K r ) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT 2 , d/gT 2 , S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction.
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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 | 0.002 |
| 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 | 0.001 | 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".