Influence of rib parameters on mechanical properties and bond behavior in concrete of fiber-reinforced polymer rebar
Bibliographic record
Abstract
Mechanical properties of fiber-reinforced polymer rebar and bond behavior between the fiber-reinforced polymer rebar and concrete are highly related to rib parameters, including rib depth and rib spacing. Therefore, rib parameters should be taken into account when fiber-reinforced polymer bars are used as the structure reinforcement. In this article, the tensile properties of glass-fiber-reinforced polymer rebars with different rib depths and rib spacings are tested. The influences of different rib depths and rib spacings on the bond behavior between glass-fiber-reinforced polymer rebar and concrete are investigated by pull-out test. Experimental results show that the rib depth has a distinctive effect on the ultimate tensile strength, elastic modulus, and ultimate elongation of glass-fiber-reinforced polymer rebar. The tensile strength and elastic modulus of glass-fiber-reinforced polymer rebar with shallow rib are remarkably higher than those of glass-fiber-reinforced polymer bars with deep rib. However, compared with the glass-fiber-reinforced polymer bars with shallow rib, the glass-fiber-reinforced polymer bars with deep rib contribute larger bond strength with concrete. Besides, the bond strength and basic anchorage length are predicted by taking rib depth and rib spacing into account. A modified Bertero–Popov–Eligehausen model is adopted to simulate the bond stress–slip behavior, and the ascending branch of bond stress–slip curve expressed by rib depth and rib spacing is also proposed. The calculated results are in good agreement with the test ones.
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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 | 0.001 |
| 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 | 0.000 | 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".