Comparisons of Two Wind Tunnel Pressure Databases and Partial Validation against Full-Scale Measurements
Bibliographic record
Abstract
Database-assisted design (DAD) is an integrated methodology that calculates wind loadings and wind-induced internal forces. It can also calculate demand-to-capacity indexes for each structural member, and by checking whether they differ significantly from unity, determine the adequacy of the members’ structural design. Its practical usefulness depends on the availability of comprehensive aerodynamic databases. A public domain aerodynamic database produced in 2003 by the University of Western Ontario (UWO) is not sufficiently extensive to satisfy design needs generally encountered in practice. For this reason, the Tokyo Polytechnic University (TPU) recently developed comprehensive sets of aerodynamic databases that are publicly available and would fill large voids present in the UWO database. This paper presents comparisons of aerodynamic pressures and forces based on TPU and UWO data for low-rise buildings to help assess the extent to which the respective aerodynamic pressure measurements are comparable. In addition, the paper presents a brief review of comparisons between full-scale and UWO wind tunnel measurements of pressures on the Texas Tech University experimental building. The results presented in the paper, though not exhaustive, suggest that TPU and UWO pressure simulations are reasonably equivalent, and may in practice be used for the design of main wind force resisting systems.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".