Optimised multi-objective design of weir structures
Bibliographic record
Abstract
Diversion head works, also called weirs or barrages, are structures constructed across rivers or canals to store water or raise the water level. The design of diversion weir structures involves calculating the depth, length and thickness of the horizontal and sloping aprons and sheet pile cut-offs. The design parameters of a diversion weir structure, which have complex non-linear relationships, are traditionally determined using empirically derived recommendations and iterations to achieve structural stability against failures due to scour, uplift, sliding, piping and overturning. However, current design approaches do not explicitly explore the trade-offs between the many relevant design objectives and thus fail to reveal possibly superior designs. A multi-objective optimisation design approach for a diversion weir structure is proposed in this article. A free and open-source code that can be used as a design tool is also provided. The method is demonstrated on a stylised design problem. The results show that the method reveals solutions with diverse balances of stability metrics and cost, with the optimal relationships of parameter values of components also varying based on the relative cost of the construction materials for sheet piles and aprons.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".