Predicting shear strength of reinforced concrete slender beams without shear reinforcement using artificial neural networks - Keskin, Riza Secer Orkun (PAMUKKALE UNIV, 2017)
Design codes aim to prevent shear failure of reinforced concrete (RC) beams since it is a brittle failure. An accurate prediction of shear strength is important for a proper design of an RC beam. There exist various equations for predicting the shear strength of RC beams. With increasing computational power, it is possible to develop numerical models delivering more accurate predictions than those equations do. In this paper, an artificial neural network (ANN) model developed for predicting the shear strength of RC slender beams without shear reinforcement is presented. The comparisons of the model with five design code equations and fourteen equations proposed by various researchers are given. The model has a better performance than the considered equations do in predicting the shear strength of the beams considered in this study. A parametric study conducted for investigating the effects of various parameters on the shear strength of RC slender beams without shear reinforcement by using the ANN model is also presented. A significant size effect on the shear strength of RC slender beams without shear reinforcement is observed through the results of the parametric study.
Keywords: Artificial neural network; Reinforced concrete; Beam; Shear strength; DIAGONAL CRACK-PROPAGATION; RC DEEP BEAMS; DESIGN PROCEDURE; PART I; STIRRUPS; SIZE; CAPACITY; FAILURE; MODEL; BEHAVIOR; Engineering