A methodology for comparing mean, fluctuating and peak wind loads of buildings
Bibliographic record
Abstract
This paper proposes a methodology to distinguish true measurement uncertainty from aerodynamic effects when comparing load coefficients from different atmospheric boundary layer wind tunnels. It considers the similarity of the wind field through profiles of mean velocity, turbulence intensities, and gust factor, along with the distribution of fluctuating flow properties, especially at small scales of turbulence. To ensure consistency, peak wind velocities and responses are estimated from time-histories matched in full-scale sampling time, hence longer records are truncated to match shorter ones. A test case involving a pressure model of a medium-rise building is proposed. It was independently tested by RWDI, CPP, and Western University under five different conditions. Time-histories of wind velocity and integrated aerodynamic base shear force, overturning, and torsional moments are analyzed and compared for nominally similar exposures. The trends in two comparisons are qualitatively consistent, with discrepancies in mean and peak coefficients not exceeding 7 % and 14 %, respectively. The analysis of the alongwind response reveals even smaller differences, especially in the mean coefficients, even across all five conditions. These findings suggest that current wind tunnel testing standards could potentially be relaxed, particularly by incorporating Partial Turbulence Simulation concepts, without compromising the reliability of aerodynamic load predictions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".