Prediction of pressure coefficient on setback building by artificial neural network
Bibliographic record
Abstract
The present study predicted the pressure (C p ), drag (C f y ), and lift (C f x ) 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 C p of intermediate wind angles by the artificial neural network (ANN) method. The C p for different WIAs is derived directly from the C p 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 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".