EUCALYPTUS SPP. GLUED LAMINATED TIMBER WITH REINFORCED FIBER FINGER-JOINTS
Bibliographic record
Abstract
Reinforcement for flexion in structural elements with finger-joints using fibers has emerged as a particularly suitable technique for timber. Thus, the objective of this study was to evaluate the performance of Glued Laminated Timber (GLULAM) produced with Eucalyptus spp. wood and three reinforcement compositions, “Glass”, “Glass2” and “Carbon” regarding parallel-to-grain tensile strength, normal tensile strength, shear strength and the three-point bending test. All the tests were performed according to the NBR 7190/1997 using the Tukey test for statistical analyzes and a 95% confidence interval. The performance of the Eucalyptus spp. GLULAM did not present significant differences in evaluation of the bonding lines. However, the “Glass 2” and “Carbon” treatments were significantly superior to the GLULAM samples without reinforcement in bending strength, reaching increments of 37.8% and 40.5%, respectively. The modulus of elasticity did not differ significantly between them. A tensile rupture was observed in the region of the finger-joints in all the evaluated samples; however, the flexural tensions were superior to the parallel-to-grain tensile strengths, indicating an influence of the timber thickness and reinforcement thickness on the performance of the reinforced joints. Thus, it is possible to conclude that applying concentrated reinforcement in the region of the finger-joints significantly improves the performance of Eucalyptus spp. GLULAM samples.
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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) | 0.001 | 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) | 0.001 | 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".