Optimal design of water‐conveying canal considering seismic stability of side slopes
Bibliographic record
Abstract
Abstract A new methodology is proposed in this study for determination of a cost‐effective canal section considering the seismic stability of homogeneous canal side slopes. In order to consider the seismic stability of canal side slopes, an external seismic slope stability model is required to incorporate with the canal optimization model. During the iterations for optimal solution, the canal optimization model uses the seismic stability model at every iteration. As a result, the efficiency of the combined model is mostly dependent on the efficiency of the seismic slope stability model. The computational efficiency of the combined model can be enhanced using an approximate model to check the stability of the side slope of the canal. Therefore, an artificial neural network (ANN) model is used in this study to approximate the seismic slope stability model. The developed ANN model is linked externally with the canal optimization model to obtain the dimensions of the cost‐effective canal section with seismic‐resistant side slopes. Pseudo‐static analysis is carried out for evaluation of the seismic stability of the slope. An example problem is solved to demonstrate the field applicability of the developed model. Copyright © 2009 John Wiley & Sons, Ltd.
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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".