Adhesively Bonded Hardwood Joints under Room Temperature and Elevated Temperatures
Bibliographic record
Abstract
Timber engineering sets high expectations on adhesive bonding as a joining method to overcome a series of limitations related to partly obsolete traditional techniques. Research on adhesively bonded timber joints has proved their superiority over mechanical fasteners in terms of strength and stiffness, but this research was often limited to softwood. Despite its abundant availability in Central Europe and its high mechanical resistance, beech is only rarely considered as a structural material. Furthermore, research on adhesively bonded timber joints almost exclusively focused on tests at room temperature. Elevated temperatures, however, are critical in conjunction with adhesives, making it paramount to shed more light on that particular aspect. Based on experimental and numerical investigations, it was found that the capacity of adhesively bonded hardwood joints increased asymptotically with overlap length to a ceiling value; furthermore, it was concluded that temperature negatively impacts capacity. Glass transition temperature, T g, marked a clear transition, but joints still sustained relatively high loads beyond T g. A probabilistic approach was validated and successfully applied to predict the joint capacity. The research contributes to fill knowledge gaps by offering the basis for subsequent dimensioning methods that at term will enable practitioners to design their structures accordingly.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".