Experimental and reliability assessment of fire resistance of glue laminated timber beams
Bibliographic record
Abstract
Glue-laminated timber (GLT) is an engineered wood product widely used in mass timber construction for its strong structural and fire-resistant properties. However, the fire performance of GLT varies significantly due to the natural and uncertain phenomena (moisture, exposure time, isotropic, homogenous properties, etc.) of fire and timber. This makes it difficult to predict the fire behaviour of the GLT structural elements. To ensure building safety, it is crucial to assess GLT's fire behaviour and post-fire structural integrity during the design stages. This study conducted the experimental tests of GLT beams (280 mm × 560 mm) without loading (1.4 m) and under a four-point bending load (5.4 m). Tests identified thermal behaviour and charring rates of GLT beam. Then, the residual stiffness of the GLT beam was calculated, and the charring rates of the beams were compared with Australian and European standards. Reliability analysis was conducted for beams for a fire exposure of 120 min, considering the charring rates observed through the analysis and simulating the fire insulations. Results show that the charring rate of GLT made with spruce pine timber varied between 0.43 and 0.81 mm/min, with a mean rate of 0.7 mm/min, aligning with both Australian and European standards. However, considering timber density and moisture content, the charring rates in Australian standards were conservative. The study also found that structural capacity significantly degrades under fire, with a 22 % reduction in flexural stiffness after 120 min of exposure. Additionally, GLT beams can safely function for 30 min under 75 % of their design moment capacity and for 60 min under 50 % capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".