Measurement of Pull-Through Resistance of Substrate Boards for Commercial Roofing Using Different Techniques
Bibliographic record
Abstract
ABSTRACT The substrate boards of commercial roofs are secured with mechanical fasteners and plates. The pull-through resistance (PTR) of the substrates depends on the material composition and fastener/plate configuration. Three different testing apparatuses are available for determining the PTR: universal testing machine Instron pull-through (IP), automated pull-through (AP), and manual pull-through (MP). To quantify the accuracy of these three test apparatuses, the National Research Council Canada completed an experimental program. The test matrix consisted of four types of substrate boards: gypsum-based board (GB), perlite-based board (PB), wood-fiber based board (WB), and polyisocyanurate insulation based (IB), two fastener plate geometries (round and hexagonal), and two types of fasteners (#12 and #14). In addition, the effect of operator ability and loading mechanisms were respectively examined for the MP and IP by employing three different operators and two different loading mechanisms, respectively. Experimental error, deviation error, and total deviation values are calculated. The findings are, respectively summarized as follows: (1) Data obtained using IP and AP showed no significant difference in the PTR values. However, the MP results varied with operators. (2) Similarly, neither the size of the specimen, the type of fasteners, nor the fastener plates resulted in significant variation in the PTR values obtained from the three apparatuses. (3) Failure analysis of the substrate boards confirmed that larger specimens absorbed energy elastically by deflecting across the board's longer span whereas smaller specimens dissipated energy by cracking across the board.
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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) | 0.001 | 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".