Behaviour of glue-laminated timber beams subjected to torsion and combined torsion and bending
Bibliographic record
Abstract
Glued-laminated (glulam) timber is increasingly being used in large-scale structures because of its benefits with respect to embodied carbon when compared to steel and concrete, its high strength-to-weight ratio, and its aesthetically pleasing appearance. However, the behaviour of glulam beams subjected to torsion or combined bending and torsion has seldomly been studied in the literature, even though these loading conditions are likely to occur in practice. Furthermore, current design standards for timber structures in North America (e.g., CSA O-86 in Canada and NDS in the United States) do not provide any guidance on how to consider torsion in design or the combined effects of bending and torsion. The overarching goal of this thesis is to conduct a series of experimental campaigns focused on addressing the knowledge gap related to the behaviour of glulam beams subjected to torsion or combined torsion and bending. The specific objectives are to understand the influence of cross-section size, lamination orientation, and aspect ratio on the behaviour of glulam beams under pure torsion. The study is then extended to consider the behaviour of glulam beams under torsion and bending, with the aim of developing an interaction diagram for this combined loading condition. In all experiments, digital image correlation is used to measure rotation and displacement distributions along the member lengths as well as high-resolution two-dimensional strain fields over the surface of the members. Results of the study demonstrate that under pure torsion, beams fail at rotations less than 5 degrees, characterized by longitudinal shear cracking along the member length, however, beams do exhibit significant post-peak deformability. Under combined loading, there is a transition between shear and flexure failure at a moment-to-torsion ratio of approximately 6.4. A torque–moment interaction diagram is proposed for glulam beams, which could provide a basis for the design of glulam members under combined actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".