Development of glass/carbon/basalt hybrid FRP rebars for reinforced-concrete beams under bending
Bibliographic record
Abstract
FRP rebars have been considered as an alternative solution to conventional steel rebars in concrete reinforced elements. There are still several challenges to overcome in order to make it more widely used, namely in what concerns rebars production process, mechanical properties and feasibility. This research work presents the development and characterization of a new type of hybrid FRP rebars combining glass, carbon and basalt fibres and analyses its application as tensile reinforcement on concrete beams tested under bending loading. Modifications on the standard pultrusion process were proposed (dual heat sections) leading to less voids and more homogeneous rebars. Twenty-three beams were used in the experimental campaign comparing beams reinforced with commercially available GFRP rebars, three different types of developed hybrid FRP rebars and beams with steel rebars. The bending performance was evaluated as well as load-deflection behaviour using two reinforcement ratios. In bending tests, Hybrid67 rebars outperformed GFRP with 36 % higher moment capacity and 49 % greater ductility, offering a promising alternative for durable, corrosion-resistant concrete structures. The best hybrid solution achieved tensile strengths up to 1013 MPa and improved bond and ductility. Experimental results were compared with analytical models described in American and Canadian standard models. A consistent overestimation, around 30 %, of the cracking moment was observed.
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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 | 0.001 | 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 | 0.001 |
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".