Prediction of pressure coefficient on setback building by artificial neural network
Bibliographic record
Abstract
The present study predicted the pressure (Cp), drag (Cfy), and lift (Cfx) coefficients on square shape and setback building models. The study considered a conventional (1:1:2) square model, a single side single-setback, and single side double-setback models. It is very challenging to measure the different aerodynamic coefficients on the setback building models for the intermediate wind incidence angles (WIAs). At first, the study calculated the different aerodynamic coefficients by computational fluid dynamics (CFD) method and then predicted the Cp of intermediate wind angles by the artificial neural network (ANN) method. The Cp for different WIAs is derived directly from the Cp versus WIAs graph. The study found the double setback model is 4.26% and 0.6% more efficient to resist the drag and lift force compared to the single setback building. Finally, the suggested setback number plays an important role to control the frequency due to pressure and velocity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".