Fragility Assessment of Roof-to-Wall Connection Failures for Wood-Frame Houses in High Winds
Bibliographic record
Abstract
The enhanced-Fujita scale (EF-scale) is used to identify tornado intensity. It uses several damage indicators (DIs), each of which has descriptions of the degrees-of-damage (DOD) along with associated wind speeds. Recent research has indicated that for wood-frame, one-family and two-family houses, differences in the structural details result in significant variations in the wind speeds estimated to cause specific levels of damage, particularly with respect to the performance of roofs. This suggests that a single damage indicator for this class of structure may be inadequate. In order to examine this point in detail, the paper focuses on failures of the roof-to-wall-connections (RTWCs) in wood-frame houses, which are frequently damaged in tornadic wind events. Fragility analyses were conducted using an extensive wind-tunnel-based dataset for the determination of the statistics of wind loads and full-scale house test data for the toe-nailed RTWC resistances. The wind load data came from wind tunnel simulations of the atmospheric boundary layer, which are likely to provide upper-bound failure wind speeds for tornadoes. The results indicate that the roof shape and the capacity of RTWCs (i.e., number/type of connections) are the primary factors affecting the failure winds for houses with dominant openings. Recommendations for modifications to the EF-scale to account for these are provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".