AN EXPLORATORY STUDY ON MIXED-MODE FRACTURE AND STRAIN DISTRIBUTION NEAR A CRACK TIP OF ADHESIVELY-LAMINATED WOOD SPECIMENS USING THE MODIFIED ARCON FIXTURE AND DIGITAL IMAGE CORRELATION
Bibliographic record
Abstract
Glue-laminated lumber-based mass timber products, such as glue-laminated timber (GLT), have been widely used in construction.This study was aimed at understanding the mixed mode fracture behaviour of a bond-line of GLT using the modified Arcon fixture and digital image correlation (DIC) technique.The laminated wood specimens used were made of white spruce (Picea glauca), as the laminations, and structural adhesives, say emulsion polymer isocyanates (EPI) and polyurethanes (PUR).The modified Arcan fixture was employed to implement mixed mode stress conditions at different angles.The effects of adhesive type on the mixed mode fracture behaviour of a bond-line of laminated wood specimens were examined.In addition, the strain distribution near the crack tip of a bond-line was illustrated using the DIC system.The results showed that the critical mode I and mode II stress intensity factors, KIc and KIIc, were 0.48 MPa m and 0.72 MPa m for the specimens made of white spruce and EPI, respectively, while those were 0.73 MPa m and 0.90 MPa m for the specimens made of white spruce and PUR, respectively.In addition, the mixed mode fracture criteria for the EPI and PUR bond-lines were proposed, resulting in a good correlation with the experimental data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".