Application of a multi-objective optimization model for the design of Piano Key Weirs with a fixed dam height
Bibliographic record
Abstract
Piano Key Weirs (PKWs) have recently been used as new or rehabilitation options in the world because of their advantages in hydraulic performance and construction costs. However, designing an efficient PKW is challenging due to a large and complex set of geometric and hydraulic parameters. Therefore, reaching an optimal PKW design depends on the examination of various geometric combinations and hydraulic parameters. In this study, we applied a multi-objective optimization model known as Non-dominated Sorting Genetic Algorithm-II to determine an optimal design by maximizing hydraulic discharge while minimizing the volume of the concrete. Here, we evaluated the capability of our approach in two separate case studies, one which represented as a rehabilitated weir and other as a new design. Both studies show that the developed approach could significantly reduce the concrete volume per unit of discharge. Moreover, the results show similar patterns in terms of the hydro-economic behavior of the model, which were discussed in three distinguished regions. The unique characteristics of these regions were elaborated, and their most cost-effective values of design parameters were identified. Finally, we discussed how the proposed model and findings of this study could be used for improving the preliminary design of PKWs in practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".