Assessing the post-fire residual stiffness of glue-laminated timber using numerical analysis and a reliability-based framework
Bibliographic record
Abstract
Ensuring the fire safety of timber structures is important in promoting timber and timber-based construction materials. Among the engineering timber used in the construction, Glue laminated timber (GLT) is a commonly used timber type. Considering timber's anisotropic and heterogeneous nature, it is essential to understand the behaviour of the timber under extreme loading conditions. Developing a numerical model to predict timber behaviour is of importance to designers, considering the cost and time for experimental tests. This study developed and validated a detailed numerical model using Abaqus software with the VUMAT user subroutine. Two models were used for the validation. Charring rates of a non-load-bearing model were studied, while the deflection of a load-bearing beam during fire was analysed. The validated model was then used to predict the residual stiffness of the GLT beam of a 5.2 m span with different numbers of plies. Further, a reliability framework was developed to assess the reliability of timber beams in the event of a fire, and 54 beam samples were numerically analysed. Results show that the number of plies in the GLT beam significantly affects the flexural stiffness when subjected to fire. It was also observed that stiffness reduction with the fire exposure is higher with lesser depths where the GLT beam with 14, 12, 9, 7 and 5 plies have flexural stiffness reduction by 25 %, 28 %, 29 %, 32 % and 53 %, respectively at the end of the 120 min fire exposure. Moreover, the residual stiffness reduction of the beams of 14 plies with Young’s modulus in the 9–13 GPa range is up to 81–76 % during the considered time. Using the reliability framework, a 40 % - 60 % reduction in the reliability index of beams was observed due to a nearly 62 % increase in span from 3.2 m to 5.2 m for normal conditions and during fire events.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".