4.7 Article

Proposing a one-way shear design model for FRP-RC members: Evaluation and reliability calibration

Journal

ENGINEERING STRUCTURES
Volume 292, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2023.116527

Keywords

FRP-RC beams; Shear strength; ACI 440; Reliability analysis; Code evaluation; Axial stiffness

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Existing shear design models for reinforced concrete are not accurate and conservative. A modified shear design model consistent with the current ACI 318-19 model is proposed, which accounts for FRP axial stiffness and has higher accuracy. The proposed model outperformed the ACI 318-19 model in statistical measures when applied to steel-RC concrete beams.
Existing shear design models for reinforced concrete are based on semi-empirical approaches and were calibrated with different experimental databases. The existing FRP-RC shear design models vary significantly in accuracy and conservatism. In particular, various studies indicated that the ACI 440-15 model is less accurate and highly conservative compared to other design models. The recently published ACI 440.11-22 adopts a similar procedure to ACI 440.1R-15 but incorporates the size effect and minimum shear strength. This study presents a detailed evaluation of different design codes based on a worldwide surveyed database spanning 344 beams. A modified shear design model consistent with the current ACI 318-19 model is proposed. This newly proposed model, which accounts for FRP axial stiffness, has demonstrated higher accuracy and hence was calibrated to achieve a reliability index of 4.0 to balance safety and conservatism. The calibrated proposed model for concrete resistance is) Vc = 0.4 (nc & rho;fl)1/3 & lambda;s & RADIC;f'c bw d(, where nc is the elastic modular ratio of longitudinal FRP bars to concrete. Finally, the applicability of generalizing and extending the proposed model to steel-RC concrete beams has been tested on a published evaluation shear database. The proposed model outperformed the ACI 318-19 model in different statistical measures.

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