Investigation of the uplift load path within the gable roof of a wood-frame residential building using a full-scale wind tunnel and non-linear finite element modelling
Bibliographic record
Abstract
This paper examines the vertical load path within the roof of a light-frame wood structure from full-scale wind tunnel experiments. The global roof uplift and overturning moments obtained from integrating pressure data are in good agreement with the those captured by load cells located at the roof-to-wall connections (RTWC). Examination of the uplift measured in each roof truss at the critical wind angles indicates that a large proportion of roof uplift loads are transferred to the gable end truss – more than would be estimated based on tributary-area calculations using the aerodynamic measurements. The amount of load transfer to the gable end is influenced most by the number of points of connection between the bottom of the gable end truss and the top of the wall below. The archetype roof indicates little dynamic amplification or attenuation in the range of frequencies in which typical wind fluctuations occur. Therefore, a quasi-static modelling approach provides a good indication of load transfer behaviour while being more computationally efficient than time-history analysis. A three-dimensional finite element model of the test roof is developed to further investigate the load distribution behaviour observed in the tests. Toe-nailed RTWCs are modelled with non-linear withdrawal and shear behaviour, allowing for wind speeds causing roof loss to be estimated. The results from the non-linear modelling indicate that the gust wind speeds causing failure of a typical light-frame wood roof can be increased from about 165 km/h to 210 km/h by providing additional nailed connections along the gable end truss roof-to-wall interface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".