Understanding Low-Sloped Roofs Under Hurricane Charley From Field to Practice
Bibliographic record
Abstract
Abstract Natural wind hazard damages have been dramatic in recent years, incurring losses of life and property around the world. Wind-induced failure is one of the major contributors to insurance claims, and it is rising. To address these growing concerns, RICOWI (Roofing Industry Committee on Weather Issues) started a Wind Investigation Program (WIP) to investigate the field performance of roofing assemblies after major windstorm events and to factually describe roof assembly performance and modes of damage. As part of this program, Hurricane Charley, which hit Punta Gorda, FL, with winds exceeding 140 mph (63 m/s), was investigated. This paper mainly focuses on the field performance of the low-sloped roofs with three important parameters that were found critical in the failure of the roofing systems, namely, • Effect of corner wind suction, • Effect of parapet, • Effect of internal pressure. For each scenario, first scientific documentation was presented, and then how the field observation reflects the fundamental principles were discussed. Based on this exercise, correlations are developed for roof wind design. In addition, wind design data from the North American codes of practice are also calculated and compared to show the impact of science and field observation on durable roof design. With these illustrations, this paper offers recommendations to advance the roof system design for hurricane-prone regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".