Nonlinear surface fit stability formula without any transition region for conventional breakwater design
Bibliographic record
Abstract
Determining the optimum weight of the armor blocks is of vital importance in the design of conventional breakwaters. The widely used formulae in the literature include the transition region from plunging to surging waves. In this paper, it is aimed to investigate a new design formula without any transition region as an alternative to widely used Van der Meer formulae. The dimensionless parameters of Van der Meer formulae as well as newly generated variables are used as inputs. Nonlinear surface fit best subset regression model is used to find the optimum input combination that keeps the nonlinear relationships. All the input parameters, their second powers, and their two-way interactions are included in the regression analyses to obtain a nonlinear surface fit. Various goodness of fit statistics are applied to check the different perspectives of the model accuracy. It is demonstrated that the proposed model gives a realistic prediction of the stability number for critical data range. Especially for high values of stability number the proposed formula outperforms the benchmark formulae of Van der Meer and Etemad-Shahidi and Bonakdar. The other advantage is that it does not contain any transition region that depends on wave conditions. Besides, there is no need to include “number of waves” and “permeability” parameters into the equation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".