Quantification of Uplift Resistance of Adhesive-Applied Low-Slope Roof Configurations Subjected to Tensile Loading Test Protocol
Bibliographic record
Abstract
The Adhesive-Applied Roofing System (AARS) is a new generation of built-up roofs gaining popularity in North American low-slope application. AARS uses no fasteners, and all components (e.g., steel deck, vapor barrier, insulation board, cover board, and membrane) are integrated by application of adhesives. Although AARS has been in use, existing standards address only mechanically attached or bonded roof assemblies. To quantify the wind-uplift performance of the AARS, an industry–university–government collaborative research project, Development of Wind Uplift Standard for Adhesive-Applied Low-Slope Roofing System, has been initiated. The project has three major tasks: experimental investigation, formulation of a numerical model, and development of wind design guide and standards. Task 1 developed test protocols to quantify the uplift and peel resistance of small-scale AARS specimens respectively subjected to tensile and shear loading. Using the standardized tensile test parameters, this paper identifies the effect of material combinations and variation in the adhesive applications on the uplift resistance of AARS subjected to tensile loading. This parametric study not only verified the applicability of the developed tensile test method for variations in the configurations, but it also identified the weakest link in AARS. Data from this small-scale testing can facilitate industries to optimize the material combinations such that it can be correlated with the systems 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 | 0.001 | 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) | 0.002 | 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".