Predicting the effect of stirrups on shear strength of reinforced normal-strength concrete (NSC) and high-strength concrete (HSC) slender beams using artificial intelligence

Abstract

The exact effect that each of the basic shear design parameters exerts on the shear capacity of reinforced concrete (RC) beams without shear reinforcement (Vc) is still unclear. Previous research on this subject often yielded contradictory results, especially for reinforced high-strength concrete (HSC) beams. Furthermore, by simply adding Vc and the contribution of stirrups Vs to calculate the ultimate shear capacity Vu, current shear design practice assumes that the addition of stirrups does not alter the effect of shear design parameters on Vc. This paper investigates the validity of such a practice. Data on 656 reinforced concrete beams were used to train an artificial neural network model to predict the shear capacity of reinforced concrete beams and evaluate the performance of several existing shear strength calculation procedures. A parametric study revealed that the effect of shear reinforcement on the shear strength of RC beams decreases at a higher reinforcement ratio. It was also observed that the concrete contribution to shear resistance, Vc, in RC beams with shear reinforcement is noticeably larger than that in beams without shear reinforcement, and therefore most current shear design procedures provide conservative predictions for the shear strength of RC beams with shear reinforcement.Key words: analysis, artificial intelligence, beam depth, compressive strength, modeling, shear span, shear strength.