Proposed Shear Design Equations for FRP-Reinforced Concrete Beams Based on Genetic Algorithms Approach
Bibliographic record
Abstract
To calculate the shear capacity of structural members reinforced with fiber-reinforced polymer (FRP), current shear design provisions generally use slightly modified versions of existing semiempirical shear design equations initially developed for steel reinforced concrete beams. Such methods generally assume that the traditional approach of superimposing concrete contribution to shear, Vc to that of stirrups, Vs can also be used to calculate the nominal shear capacity, Vn of FRP-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. These methods also noticeably vary in the manner they account for the effect of basic shear design parameters on shear strength. This paper presents simple yet improved equations to calculate the shear capacity of FRP-reinforced concrete beams based on the genetic algorithms approach. The performance of the proposed equations is compared to that of four commonly used shear design methods for FRP-reinforced concrete beams, namely the ACI 440, CSA S806, JSCE, and ISIS Canada. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP-reinforced concrete beams. Moreover, the shear capacity of FRP-reinforced concrete beams calculated using the proposed equations is in better agreement with available experimental data than that calculated using shear equations provided by current provisions. This study also shows that the axial rigidity of FRP longitudinal bars is best represented by a cubic root function and that the contribution of FRP stirrups to shear strength is a square root function of the stirrups ultimate capacity rather than a linear function as proposed by current shear provisions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".