Shear Capacity of FRP Stirrups in FRP-Reinforced Concrete Beams Based on Genetic Algorithms Approach
Bibliographic record
Abstract
Current shear design guidelines propose that the nominal shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP) can be calculated using existing shear design equations for steel-reinforced concrete beams provided that the axial rigidity of FRP longitudinal bars and the capacity of FRP stirrups at the bent portions are accounted for. However, they noticeably vary in the manner they account for concrete contribution to shear as well as the shear capacity of FRP stirrups. This paper presents a simple yet more accurate equation to determine the shear capacity of FRP stirrups in FRP-reinforced concrete beams based on genetic algorithms approach. The shear capacity of FRP stirrups calculated using the proposed equation is compared to those obtained using equations provided by four commonly used shear design guidelines for FRP reinforced concrete beams, namely the ACI 440, CSA S806, JSCE, and ISIS Canada. Results show that current guidelines overestimate the capacity of FRP stirrups and that such capacity is best represented by a square root function of the stirrups ultimate capacity rather than a linear function as proposed by the guidelines. The shear capacity of FRP stirrups calculated using the proposed equation are in better agreement with available experimental data than those calculated using shear equations recommended by current guidelines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".