PERFORMANCE OF WOOD-FRAMED RESIDENTIAL STRUCTURES UNDER EXTREME WIND LOADS
Bibliographic record
Abstract
Failures of wood-framed residential structures are among the most common and expensive types of wind damage in densely populated regions.Numerous recent studies have focused on mitigating residential damage during tornadoes and hurricanes.Past work has identified weak links in the vertical load path of wood-framed homes under uplift, focusing primarily on the roofs since their failure is common.In recent work, structural details such as connections and fasteners have been determined to have a large impact on the resilience of wood-framed homes.In this paper, common residential failure modes are reviewed, ongoing work to prevent expensive residential damage is presented, and failure wind speed estimates currently used in tornado assessment are revisited.The results of preliminary structural analyses verify the common understanding that toe-nailed roof-to-wall connections are likely to be among the most vulnerable elements in the structure of a wood-framed house.However, it is also found that certain framing members and connections display significant vulnerability under the same wind uplift, and the possibility of framing failure is not to be discounted.The analysis results and damage survey observations are used to expand the understanding of wood-framed residential roof failures, as they relate to the Enhanced Fujita scale, and address potential gaps in current residential construction practice.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".