Neural network prediction of joint shear strength of exterior beam-column joint

Abstract

Beam-Column joints are the critical locations in the reinforced concrete structures as they experience a massive amount of deformations under earthquake. The shear failure of the beam column joint should be avoided for the safety of the structure. In the present study, prediction of joint shear capacity of exterior Beam-column joint is proposed using artificial neural network (ANN). Experimental investigations performed by different authors have been examined and used to prepare the data sets for training, testing and validating the neural network. Parameters responsible for the shear strength of the exterior Beam-Column Joints are identified and the artificial neural network model is proposed to predict the joint shear strength. Input parameters for the ANN model are width and depth of the joint, concrete compressive strength, length of beam, top and bottom longitudinal reinforcement in the beam, yield strength of longitudinal reinforcement in beam, ratio of beam to column depth, joint Shear reinforcement index, beam bar index and column load index. The performance of the neural network model is evaluated by the statistical relations like Coefficient of correlation, Root mean square error and Scatter index. The proposed model is compared with an empirical formula and different equations suggested by the design codes. The results show that the proposed neural network model can effectively predict the joint shear strength of the Exterior Beam-Column joint.