Post-fire structural performance of glued-laminated timber columns subjected to real fires
Bibliographic record
Abstract
This paper presents the results of an experimental test program conducted on fire-damaged glued-laminated timber (glulam) columns. The columns were part of the Mass Timber Demonstration Fire Test Program (MTDFTP) in Ottawa, Canada, during which a series of full-scale fire tests were performed on a two-storey mass timber structure. The structure was designed to represent the fourth and fifth storey of a hypothetical 12-storey mass timber building. The specimens were subjected to qualitative and quantitative post-fire structural assessments following the fire tests. The charred layer of each column specimen was removed by physical means and the residual cross-sections were measured and evaluated to determine the char depth. Full-scale axial compression tests were conducted to verify the post-fire structural demand of the columns. The findings indicated that the charring rates consistently exceed those prescribed in contemporary design standards. The column specimens were capable of withstanding expected post-fire axial loads, secondary effects caused by the fire damage need to be considered when predicting post-fire axial capacities. The research outcomes of this study are expected to support Canadian design and fire code provisions as they pertain to the use of mass timber in mid-rise structures, particularly in terms of conducting post-fire structural assessments.
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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 | 0.001 | 0.001 |
| 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".