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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".