Aerodynamic mitigation of low-rise building with complex roof geometry
Bibliographic record
Abstract
During strong wind events, building roofs are subjected to high wind uplift forces (suctions), which often lead to severe roofing component damage, or even roof total failure, flying debris, and water intrusion, hence, interior damages. Typical roof shapes (e.g., gable and hip) are generally designed using provision codes and standards to accurately estimate peak load impacting the roofs during wind events for design purposes. Complex roof geometry can be efficiently examined using wind tunnel testing and computational modeling to provide quantitative assessment for wind to narrow down the design alternatives and to examine the improvement gained from mitigation techniques. In this study, an isolated low-rise building with a complex roof shape is examined using large eddy simulation (LES) to numerically assess wind load prediction by validating it with wind tunnel results. This study presents two roof modification scenarios using parapets added to roof corners and ridgelines to displace the flow from the separation locations to reduce the wind impact on the roof. The current study aims to 1) evaluate wind load on an isolated low-rise building with complex roof geometry for various angles of attack and 2) mitigate the roof aerodynamically using parapets, added corners, and ridgeline to reduce the wind impact on the roof. The validation shows that both the mean and RMS of the pressure coefficients are in good agreement with the wind tunnel results. The research results suggest that parapets with 500 mm height located at the corner and edges of complex roof geometry can effectively reduce extreme corner suction by 29% and roof uplift by 5.6%.
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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 | 0.000 |
| 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 | 0.001 |
| 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".