Prediction of reflection coefficient of a perforated Quarter Circle Breakwater using artificial neural network (ann)
Bibliographic record
Abstract
Abstract A breakwater is structure which is generally adopted in not only protecting the shoreline, but also in creating tranquil zone on the lee side of the structure minimizing the various movements on the anchored ships / vessels due to the wave / tidal action in the region resulting in easy handling of goods. Over the years, breakwater was generally constructed using rubble mounds. Due to the increase in demand for the coastal development all over the world, many innovative Breakwater were evolved as against the rubble mound. In the recent times, in order to economize the innovative breakwater construction, Semi Circular caisson type Breakwater has been studied. Based on Semi circularBreakwater (SBW), Quarter circular Breakwater (QBW) has been evolved. The hydrodynamic performance of a coastal structure is important, because it involves many parameters to be considered while designing a safe and economical structure. The hydro-dynamic performance of a Quarter circular breakwater is studied in a monochromatic wave flume in the Department of Applied Mechanics and Hydraulics, National Institute of Technology, Surathkal Karnataka, India. In the present paper reflection coefficient (Kr) of a perforated Quarter circular Breakwater (QBW) with various S/D ( spacing to diameter ratio) values is predicted applying Artificial Neural Network (ANN) technique using MATLAB. Four networks were constructed by varying the number of hidden layers based on the input parameters, which affects the performance of the breakwater. The predicted values of reflection coefficient using ANN, are compared with the experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 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".