Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran
Why this work is in the frame
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Bibliographic record
Abstract
Piezometric heads in the core of Sattarkhan earthfill dam in Iran have been analyzed in this paper via Artificial Neural Network (ANN). Single and integrated ANN models were trained and verified using each piezometer’s data, and also the water levels on the up and downstream of the dam. Therefore, in the single ANN modeling a single ANN was developed for each piezometer, whereas in the integrated ANN modeling only a unique ANN was trained for all piezometers at different cross sections of the dam. Three-layered Perceptron ANN trained with Back Propagation Levenberg-Marquardt scheme was employed in the single modeling; while, two different ANN algorithms, the feed-forward back-propagation (FFBP) and the radial basis function (RBF) were employed to develop integrated ANNs. The number of hidden neurons were determined 5 and 7 for single ANNs, whereas 6 hidden neurons for the integrated FFBP ANN, and the spread value of 0.5 for the integrated RBF. The results show good agreement between computed and observed water heads at different monitoring piezometers with validation determination coefficients higher than 0.7984 in the single and 0.87 and 0.67 in the FFBP and RBF integrated modeling, respectively. Thereafter, the results of the ANNs were satisfactorily compared with the results of a physically based model (Finite Element Model, FEM).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it