Full Scale Tests on the Performance of Hybrid Timber Connections in Real Fires
Bibliographic record
Abstract
Connections form an integral part of any building system.A hybrid building system is created when two or more structural construction materials are involved in its construction.There are concerns when using hybrid connections especially when they are exposed to fire.This is because different materials with varying ambient and thermal properties must perform together to ensure a safe and stable structural system.This research seeks to present the fire behavior and resistance of unprotected hybrid connection systems involving a glulam timber beam and steel columns in typical real fires referred to as nonstandard fires, and their comparison to performance under temperatures defined by CAN/ULC S-101.Three different shear tab connection systems: Concealed, Exposed and Seated were studied.These connection systems transfer beam end reactions to the columns.Each connection system was tested for two load ratios of 60% and 100% with a 12.7 mm Grade A325 bolts.The time to failure of each assembly under the modelled non-standard fire curve reduced with increasing load ratios.Lower load ratio of 60% resulted in an increase in the times to failure by 50% -75%.Fire resistance ratings in the modelled real fire curve were low, with a higher resistance time of 21 minutes recorded for the Seated Connection Assembly under 60% load ratio.Using the cumulative radiative energy area method to predict the severity of the standard CAN/ULC-S101 and real fire curves gave good results.The method predicted conservative equivalent times of failure for the Seated Connection Assembly under both load ratios of 60% and 100%.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".