GLUED-LAMINATED TIMBER UNDER EXTREME COLD TEMPERATURES SUBJECTED TO IMPACT LOADING
Bibliographic record
Abstract
With the increasing turbulence in the global security environment, comes the requirement for an increasing presence in the Canadian Arctic to respond to regional challenges and provide security.Such presence requires temporary and permanent installations, which must inherently carry with it some considerations for extreme load events, such as blast and impacts.In tandem with this is the expanding need to build more environmentally sustainable buildings, for which wood has been identified in recent years.However, questions remain on how wood responds to blast and impact loads when exposed to cold temperatures, typical of the arctic region.To respond to this gap in research, an experimental program was carried out to investigate the flexural behaviour of glued-laminated timber (glulam) subjected to impact loading under ambient and winter arctic temperatures.Dynamic testing was conducted using a drop weight impact hammer.For strain rates between 1.13 to 1.38 s -1 , an average dynamic increase factor of 1.23 on the maximum resistance at ambient temperatures was observed.The cold temperature beams were seen to experience a 13% increase in strength beyond their normal temperature counterparts under dynamic effects.Increases in stiffness due to cold temperature were also observed under static and dynamic loading.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".