Wind Uplift Performance of Composite Metal Roof Assemblies
Bibliographic record
Abstract
A common factor in roof failures is wind forces, which inflict considerable damage every year, even to new roof structures. Metal roofs are a popular low-sloped roof assembly. On the basis of their layout, metal roofs can be categorized as either composite or noncomposite assemblies. In North American practice, five main test procedures—ASTM E1592, ANSI/FM 4474, UL 580, UL 1817, and CSA A123.21-04—are used to determine the wind uplift performance of metal roofs. The fundamental differences between these test protocols lie in the way they represent wind effects on the performance of metal roofing systems. Of the five, CSA A 123.21-04 is the only one that assesses the wind uplift resistance under dynamic wind load conditions. To evaluate the wind uplift performance of noncomposite and composite metal roofing assemblies, eight assemblies with two different types of panels—SNAP-IT and MR-24—were tested by using the CSA A123.21-04 dynamic test protocol. By relating air intrusion characteristics of the subsurface components to panel behavior, this paper shows how composite assemblies resist wind uplift pressures better than noncomposite assemblies. This paper reveals that increased air intrusion resistance of the sub surface components in composite assemblies results in increased suction resistance, decreased panel deflection, decreased stress on the panels, and increased wind uplift resistance.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".