Design of Stick-Framed Wood Roofs Under Extreme Wind Loads
Bibliographic record
Abstract
Light-frame wood construction comprises nearly 90% of the housing industry inCanada and the United States. The roofs of these houses can be constructed eitherentirely on site or using prefabricated trusses. Assembling the roof structure on site,otherwise known as stick-framing, is a framing technique with current code guidelinesthat are based on past practice and limited consideration of wind loads. This makesthese roof structures susceptible to failure in high-speed wind events, such as tornadoes. Around 90% of all tornadoes in Canada fall in or below the EF-2 category,making it a possible target for design of wood-frame houses to be resistant to theseevents. This research proposes improved stick-framing guidelines that would work forEF-2 tornadoes. Using non-linear finite element analysis, a stick-framed roof was designed following the guidelines in the National Building Code of Canada. Non-linearlinks were used to model all of the connections between the members in the roof structure, with frame elements used to represent the members. Increasing wind loads were applied to the structure and the first elements of the roof that failed were improved using an iterative performance-based design approach until the performance target of resistance to EF-2 tornadoes was achieved. The failure of the roof-to-wall-connections and the lack of members used in the framing were the two main issues highlighted and addressed. Damage survey photos were used to compare failures observed in the model with failures after real tornado events, which demonstrate many similar failure modes. This research recommends the requirements to ensure stick-framed roofs can withstand EF-2 tornadoes. Most notable is an improved gable end frame, which gives the structure more roof-to-wall connections, as well as a more structurally sound frame where wind loads are the highest. Other additions include struts, hurricane ties at all roof-to-wall connection locations and increased number of nails in various connections throughout the repeating inner frames. Minimum member sizes and strengths for each type of member used in the roof structure are recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".