Fire Performance of Timber Connections, Part 1: Fire Resistance Tests on Bolted Wood-Steel-Wood and Steel-Wood-Steel Connections
Bibliographic record
Abstract
This paper presents the results of a series of fire-resistance tests on bolted wood-steel-wood (WSW) connections and bolted steel-wood-steel (SWS) connections. In total, 16 WSW specimens and 6 SWS specimens were tested in accordance with the provisions of CAN/ULC-S101. All the specimens were subjected to a constant tensile load parallel to grain during the tests. The effects of load level, wood thickness, fastener diameter, number of fasteners, edge distance and protection were studied. The test results show that the fire-resistance ratings of all the tested WSW connections without protection were less than 45 minutes (a target rating for Canadian code compliance) and the fire-resistance ratings of all the tested SWS connections without protection were less than 25 minutes. Specimens with thicker wood side members were found to exhibit better fire resistances. Decreasing the load ratio increased the fire resistance. The test results on protected specimens show that the protection provided by a single-layer of 15.9 mm type X gypsum board increased the fire resistance by more than 30 minutes, whereas a double-layer of 12.7 mm Douglas fir plywood increased the fire resistance by 15 minutes. Furthermore, this research has generated necessary data and knowledge for the validation of a numerical heat transfer model and an analytical structural model.
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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 | 0.000 | 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) | 0.002 | 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".