An Experimental and Analytical Study on the Bending Performance of CFRP-Reinforced Glulam Beams
Bibliographic record
Abstract
The fiber-reinforced polymer is one kind of composite material made of synthetic fiber and resin, which has attracted research interests for the reinforcement of timber elements. In this study, 18 glued-laminated (glulam) beams, unreinforced or reinforced with internally embedded carbon fiber–reinforced polymer (CFRP) sheets, were tested under four-point bending loads. For the reinforced glulam beams, the influences of the strengthening ratio, the modulus of elasticity of the CFRP, and the CFRP arrangement on their bending performance were experimentally investigated. Subsequently, a finite element model developed was verified with the experimental results; furthermore, a general theoretical model considering the typical tensile failure mode was employed to predict the bending–resisting capacities of the reinforced glulam beams. It is found that the reinforced glulam beams are featured with relatively ductile bending failure, compared to the brittle tensile failure of the unreinforced ones. Besides, the compressive properties of the uppermost grain of the glulam can be fully utilized in the CFRP-reinforced beams. For the beams with a 0.040% strengthening ratio, the bending–resisting capacity and the maximum deflection can be enhanced approximately by 6.51 and 12.02%, respectively. The difference between the experimental results and the numerical results and that between the experimental results and analytical results are within 20 and 10%, respectively.
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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) | 0.001 | 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 | 0.001 | 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".