Shear and Flexural Strengthening of Glued-Laminated Timber Beams Using Externally Bonded Fiber-Reinforced Polymer Sheets
Bibliographic record
Abstract
This study evaluated the effectiveness of using externally bonded fiber-reinforced polymer (FRP) sheets at increasing the load-carrying capacity of glued-laminated (glulam) timber beams. The investigation examined a range of strengthening configurations, including the use of U-wraps for shear strength enhancement and the combination of U-wraps and longitudinal FRP for combined shear and flexural strengthening. Parameters of interest in the study included the influence of fiber type (glass versus carbon), fiber orientation (uni- versus bidirectional FRP U-wraps), and fiber bond (bonded versus unbonded U-wraps). Consideration was also given to the influence of U-wrap anchorage on the retrofit performance. Retrofit performance was evaluated by testing eight large-scale glulam beams with a low shear-span-to-depth ratio (a/d = 2.39). Results of the study demonstrated that both the fiber orientation of the U-wraps and the bond between the FRP and wood impacted the effectiveness of the retrofit. Overall, the use of FRP U-wraps was found to increase the beam strength and ductility relative to the control beam by 1.2 and 1.7 times, respectively. The combination of U-wraps and longitudinal FRP sheets for flexural strengthening increased the beam capacity by up to 1.4 times the control beam. The results demonstrated that longitudinal FRP strains reached up to 7,000 µε, while strains in the FRP U-wraps exceeded 2,900 µε in some configurations. The distributions of the longitudinal and transverse strains in the FRP measured using distributed fiber optic sensors are also discussed.
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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".