Modelling the Effects of Hydraulic Force on Strain in Hydraulic Structures Using ANN (Haditha Dam in Iraq as a Case Study)
Bibliographic record
Abstract
The strain on any structure is a critical issue worldwide, resulting from loads on the structure. An exact prediction at all the expected strains ranges on the dam will pave the way for better dam management under different discharges. The main aims of the present paper are to study the behaviour of strains on the dam and create a model based on ANN techniques that can be used to predict the strain on the model of the Haditha dam. The ANN is a computational model that simulates the method neurons work in the human brain. The research includes a study of the strains on the dam body and the gate. The input of the present model includes gate opening, discharge, depth of upstream water, and force on the dam body and gate. The model has been applied by using 150 actual testes of strain in the hydraulic laboratory. The model has been achieved by using a MATLAB software with hyperbolic sigmoid transfer function and three nodes. The accuracy of the model was achieved by using some statistical indicators. The results show the ANN is capable of predicting the strain on the Haditha dam with high accuracy. The regression for both strains on the dam body and the gate was more than 89% for all training, validation, testing, and all samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".