Bending performance of cross-laminated timber reinforced with glass fibre-reinforced polymer and aluminium sheets
Bibliographic record
Abstract
There is limited research on enhancing the flexural performance of cross-laminated timber (CLT) panels made from fast-growing hardwood species like poplar, particularly using external reinforcements. This study aims to investigate the effect of aluminium (AL) and glass fibre-reinforced polymer (GFRP) sheets on the bending behaviour of 3-ply poplar CLT panels in both longitudinal (L) and transverse (T) directions. Four reinforcement configurations were tested. The results showed that AL reinforcement increased MOR by 53.9% (L) and 208% (T), and MOE by 56.9% (L) and 617% (T) of CLT samples. GFRP led to MOR gains of 37.4% (L) and 61.8% (T), and MOE gains of 38.3% (L) and 223% (T) in CLT samples. The most effective flexural performance in CLT samples was achieved with three layers of reinforcement, particularly with AL. Ductility of CLT samples also improved significantly: AL increased it by 76.6% (L) and 28% (T), while GFRP improved it by 47% (L) and 55% (T). These findings suggest that external reinforcement – especially with AL – can effectively enhance bending performance and ductility of poplar CLT, providing a viable strategy to improve its structural performance for broader application in engineered timber construction.
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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 | 0.000 | 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".