Interactive algorithm for geometric modelling double-curvature arch dams
Bibliographic record
Abstract
A rapid and efficient algorithm for interactive geometric modelling of arch dams is presented. It combines the advantages of the traditional geometricdesign with innovative computational capabilities offering simple procedures for otherwise complex process of laying out double-curvature arch dam-reservoir coupled systems. The key parameters taken into account are: terrain topography, shape and thickness of crown cantilever, reference cylinder, thickness and curvature of individual arches, excavation depth, concrete volume, vertical and peripheral construction joints and automatic generation of finite element and boundary element models. The proposed algorithm was implemented in and runs parallel to the ADAD-IZIIS FEM-BEM, a finite element-boundary element software for structural analyses of concrete arch dams. To demonstrate the performances of the proposed algorithm, an example of a 130m high double-curvature arch dam was considered in a narrow V-shape canyon. The number of graphical options available at the push of a button, such as vertical and horizontal cross sections and 3D perspectives, allows the user to rapidly conduct the dam design within the optimization process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".