Performance of Post-Tensioned Timber and Engineered Timber Adhesives with Fire Exposure
Bibliographic record
Abstract
Increased environmental conscientiousness and the abundance of timber in Canada has lead to the desire for more timber construction. To increase opportunity for timber products in construction, novel building systems including Post-Tensioned (PT) timber are required. A numerical model was developed and validated in FEM software Abaqus to model PT timber in fire conditions, with highly promising results. Beam failure times were modelled within 5%, and load-deflection behaviour and failure mechanisms were accurately demonstrated. Additionally, the performance of timber adhesives after fire damage was examined. Based on the experimental results, additional zero-strength layer thicknesses were estimated conservatively to be 23 mm beyond the char front (95th percentile) to account for the loss of strength (subject to various limitations). It is recommended that a new standardized test be developed for timber adhesives which quantifies the performance beyond the char layer in burnt engineered timber so that individual adhesives may be evaluated.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".