Influence of concrete cracks and transverse bar properties on the strength of FRP-reinforced concrete beams
Bibliographic record
Abstract
The unique properties of Fibre Reinforced Polymers (FRP) bars such as their non-corrosive nature, high tensile strength to weight ratio and wide range of material properties offer practical solutions for various problems of traditional steel reinforcement. However, since the failure of FRP-reinforced concrete beams is, usually, different from that of steel-reinforced ones, an analytical model was developed earlier by the author representing the characteristics of concrete cracks, the beam shear-flexural interaction and the dowel action of FRP bars. The model traces the failure mechanism and eventually predicts the beams strength and the corresponding mode of failure. Through a series of previous publications, the model results were verified experimentally and pointed at a considerable over-estimation of the beam strength values obtained by past editions of related design codes. Recently, a significant step has been taken by the Canadian standards as quality control specifications have been issued to classify the manufactured FRP bars and to ensure their expected performance. Therefore, a parametric study, based on the developed model, is presented herein in order to investigate the influence of such step on the predicted beam strength and mode of failure. Further, the design philosophy for FRP-reinforced concrete beams presented by most of the current design codes is discussed in the light of the obtained results.
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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) | 0.000 | 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".