Over the past few years, a wide use of externally-bonded fiber-reinforced polymer composites (EB-FRP), for rehabilitation, strengthening and repair of existing/deteriorated reinforced/prestressed-concrete (RC/PC) structures has been observed. This paper presents a nonlinear iterative analytical approach conducted to investigate the effects of concrete strength, steel-reinforcement ratio and externally-reinforcement (FRP) stiffness on the flexural behavior and the curvature ductility index of the FRP-strengthened reinforced high-strength concrete (RHSC) beams. Analysis results using the proposed technique have shown very good agreement with the experimental data of FRP-strengthened/non-strengthened RHSC beams, regarding moment–curvature response, ultimate moment and failure mode. Also, a newly prediction equation for the curvature ductility index of FRP strengthened RHSC beams has been developed and verified. Then, converting equation of the curvature ductility index to energy one is proposed. Results indicate that the proposed predictions for the curvature and energy ductility indices are accurate to within 1.87% and 3.03% error for practical applications, respectively. Finally, limit values for these bending ductility indices, based on different design codes’ criterion, are assessed and discussed.
Fiber-Reinforced Polymers (FRP) were developed as a new method over the past decades due to their many beneficial mechanical properties, and they are commonly applied to strengthen masonry structures. In this paper, the Artificial Neural Network (ANN), K-fold Cross-Validation (KFCV) technique, Multivariate Adaptive Regression Spline (MARS) method, and M5 Model Tree (M5MT) method were utilized to predict the ultimate strength of FRP strips applied on masonry substrates. The results obtained via ANN, KFCV, MARS, and M5MT were compared with the existing models. The results clearly indicate that the considered approaches have better efficiency and higher precision compared to the models available in the literature. The correlation coefficient values for the considered models (i.e., ANN, KFCV, MARS, and M5MT) are promising results, with up to 99% reliability.
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