Pullout Resistance of Roofing Fasteners Using Different Methods
Bibliographic record
Abstract
Wind uplift rating is one of the key performance requirements for single-ply mechanically attached systems. It depends on the properties of the membrane (mechanical, physical and chemical), substrate (compressive strength and dimensional stability), and deck (thickness and deflection). An attachment system (fastener, plate and seam) integrates the above components to form an assembly. Fastener pullout resistance (FPR) from the deck is one of the essential design parameters in the system specification. As well, the FPR is an indicator of the existing deck condition in reroofing/recover applications. In the field, the FPR values are obtained using pullout testers. To quantify the accuracy of the manual pullout testers under various environmental conditions, an experimental program is in progress at the National Research Council's Dynamic Roofing Facility (DRF). From this ongoing research, this paper compares the FPR data from the field pullout tester with those obtained using the laboratory universal Instron testing machine. In the manual pullout tester, consistency of the FPR data depends on the operator. Considering this variable and to generalize the data, the above experiments are performed on two decks (steel and wood) with three different fastener types. The study finds that the field manual pullout tester underestimates the FPR data in comparison to the data obtained from the laboratory machine. This trend is found true irrespective of the selected decks and fasteners. In the roofing market, automatic pullout testers are also available that function using a battery-powered source. Selected experiments were performed to identify the influence of the pull out speed on the FPR data using an automatic pullout tester.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".