Modelling the Fire Performance of Hybrid Steel-Timber Connections
Bibliographic record
Abstract
In a building structure, wood can be used in conjunction with steel or concrete material to form what is known as a hybrid building system.A hybrid system combines the efficient properties of the different materials to achieve design requirements such as structural or fire safety.In this research, a typical steel-timber hybrid system is considered.This steel-timber hybrid system consists of a glulam wooden beam connected to a steel column.The connection of the beam to the column is composed of three different types of shear tab connections: concealed, exposed and seated connections.These connections transfer vertical loads between the beams and columns in a hybrid structure.The fire resistance of these connections is evaluated using a finite element model and compared with the full-scale experimental fire resistance tests which had been conducted earlier in a separate project.The major parameters studied included load ratio, heat transfer, charring properties of wood, failure mode of the wood, and their influence on the time to failure of the connections.The finite element model results were in good agreement with the observations made from the experimental tests.The variation between the test and the model results was within a ±11% envelope.In conclusion, the seated connection had a better fire resistance as compared to the concealed and exposed connections.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".