Proposed Shear Design Equations for FRP-Reinforced Concrete Beams Based on Genetic Algorithms Approach
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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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Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it