Fire Testing and Modeling of a Novel Hybrid Timber Floor System
Bibliographic record
Abstract
To maximize the amount of carbon-sequestering mass timber and demonstrate the potential for mass timber across a range of building types and scales, DIALOG and EllisDon have developed a hybrid timber floor system (HTFS) that is composed of post-tensioned (PT) concrete beams, cross laminated timber (CLT) panels, and concrete topping connected to each other through self-tapping screws and kerf plates. This paper presents the fire performance of this novel HTFS through a combination of testing and modeling. Two mid-span sectional specimens of HTFS without concrete topping were exposed to the standard fire of CAN/ULC S101. The char depth and char rate of CLT were measured, and the temperature at specific locations were recorded to verify the design and the developed advanced finite element models. Advanced 2D models were developed to investigate the influence of steel properties and the effect from concrete spalling, and the modeling approach adopted in the refined 3D model. The developed models can estimate the char depth with difference less than 5%, and the temperature in reinforcing bars, PT duct, and concrete comparable to the average of the test results. The experimental and simulation results give an insight into the fire performance of this novel HTFS.
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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 | 0.001 | 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".