Wind uplift performance of roofing systems with vapour barrier
Bibliographic record
Abstract
Wind dynamics, on a conventional roofing system, lift the membrane and cause fluttering, introducing stresses at the attachment locations. To identify the component of the system that has the weakest resistance against wind uplift forces, a dynamic method of evaluating roofing systems is beneficial. A vapour barrier is defined as a material of low permeance, which limits moisture transport through an assembly due to moisture diffusion. In addition, some vapour barriers can also resist the airflow movement. The objective of this paper is to investigate the changes in the wind uplift performance in a roof assembly with and without the presence of such vapour barrier. Two vapour barrier types were evaluated as components of full-scale mockups. These include polyethylene film conventionally used in low slope roof assemblies in Canada and modified bituminous surfaced reinforced film, a new product in the roofing market. Wind uplift evaluations are being carried out at the Dynamic Roofing Facility (DRF) of the National Research Council of Canada, using the SIGDERS (Special Interest Group on Dynamic Evaluation of Roofing Systems) dynamic wind test protocol. Systems with vapour barriers performed better than the systems without vapour barriers. Use of vapour barrier minimizes asymmetrical stress concentration and improves the wind rating of the mechanically attached roofing 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".