Which is the weakest link: how it can be stronger? Lesson learned from the 20 years of hurricane investigations
Bibliographic record
Abstract
As insurance claims are rising, wind induced failures are one of the major concerns for building envelope designers. To understand the weakest links on the roof assembly, the Roofing Industry Committee on Weather Issues (RICOWI – formerly known as Roofing Committee on Wind Issues) started a Wind Investigation Program (WIP) in 1996. The objectives of WIP are as follows: 1. Investigate the field performance of roofing assemblies after major wind storm events 2. Factually describe roof assembly performance and modes of damage 3. Formally report the results for substantiated wind speeds. In order for RICOWI investigations to be successful the investigation teams should be balanced, unbiased and trained in wind damage assessment. For a team to be balanced it should consist of a manufacturer, a roofing consultant, university or insurance organization personal, and a manufacturer from another part of the roofing industry. As part of this program, over the past 20 years, several wind investigations were completed. This paper mainly focuses on the performance investigations of low-sloped roofs. The two most important weak links identified from these investigations are: 1) roof metal edges and integration of roof/wall interface and 2) rooftop equipment. Each of the above weak links is systematically analyzed and then followed by field observation that reflects the fundaments. Based on this exercise, correlations are developed for the effects of wind on roof design. Additionally, wind design data from the North American codes of practice are also calculated and compared to show the importance of science and field observations on roof design to determine how we can make them more durable. With these illustrations, this paper offers recommendations to advance roof system designs for hurricane-prone regions that are resilient.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".