Probabilistic characterization of roof panel uplift capacity under wind loading
Bibliographic record
Abstract
The integrity of the roof system is important to the safety of inhabitants and prevents excessive damage to light-frame wood structures. The uplift capacity of fastened roof panels has been investigated using experimental tests and numerical models. Monotonically increasing uniform static pressure is often employed in experimental investigations and numerical modeling is carried out by assuming that the tributary area method is adequate and the fasteners can be modeled as linear elastic springs, even though the force–deformation relationship for nail withdrawal is nonlinear and uncertain. This study is aimed at assessing the statistical characteristics and modeling the uplift capacity for the roof panel under stochastic wind pressure by incorporating the uncertainty in nail withdrawal behaviour. The results show that the nonlinear behaviour of nail withdrawal needs to be considered to improve the accuracy of the estimated uplift capacity; the statistics and the probability model of the uplift capacity are affected by the degree of correlation of the fastener behaviour within the panel; and that nail spacing and missing nails influence the uplift capacity significantly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".