Data-driven models predict shear capacity in FRP-reinforced concrete beams, highlighting elastic modulus and reinforcement ratio.
This study develops data-driven models for predicting the shear capacity of reinforced concrete (RC) beams longitudinally reinforced with fiber-reinforced polymer (FRP) bars and lacking transverse reinforcement. Owing to the comparatively low elastic modulus and linear–elastic–brittle behavior of FRP bars, reliable shear prediction remains a design challenge. A curated database of 402 tests was compiled from the literature, spanning wide ranges of beam size (width b, effective depth d), concrete compressive strength (f′c), FRP elastic modulus (Ef), longitudinal reinforcement ratio (ρf), and shear span-to-depth ratio (a/d). Multiple multivariate regression formulations—both linear and nonlinear—were developed using combinations of these variables, including a mechanics-informed reinforcement index (ρf·Ef). Model predictions were benchmarked against 15 existing expressions drawn from design codes, standards, and prior studies. Across the full database, the proposed models demonstrated consistently stronger agreement with experimental results than the existing predictors, yielding higher correlation and lower prediction error. The resulting closed-form equations are transparent and straightforward to implement, offering improved accuracy for the preliminary design and assessment of FRP-RC beams without stirrups while highlighting the influential roles of Ef, ρf, and a/d within the observed parameter ranges.
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Rasheed et al. (2025) studied this question.
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