Non-iterative design method for flexible channels with bends
Bibliographic record
Abstract
Flexible linings provide a means of stabilising roadside channels. These linings conform to potential changes in channel shape while maintaining overall lining integrity. The current design method of flexible linings is based on trial and error and typically involves three sets of iterations. The method becomes extremely complex when lining type and channel dimensions are to be modified to satisfy three design criteria: discharge, permissible shear stresses and side slope stability. This paper presents an optimisation model that directly provides the best channel lining subject to the design criteria. The proposed model can handle a large number of constraints, and provides the optimal solution in seconds using the Excel Solver software. The model is developed for riprap, cobble and gravel linings. The user needs to specify available stone sizes, and then the model selects the best size for channel straight and curved segments. The application of the model is illustrated using numerical examples, and sensitivity analysis is performed. The proposed model has been validated by comparing its results with those of the trial method. The proposed model provides efficiency and flexibility in the design of roadside channels, and should be a useful design tool of interest to practitioners and researchers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".