Abstract:In various areas of civil engineering, Artificial Neural Network (ANN) model is regarded as an effective method to solve complex problems. In this study, the shear bearing capacity of RC shear walls was predicted using ANN models based on the experimental data. A database on the performance of RC shear walls established from the results of 120 160 experimental data under cyclic loading was built. Moreover, 100140 experimental samples were chosen to train the ANN models, while 20 experimental samples were used for validation. There are had fourteen inputs, including concrete compressive strength, aspect ratio, axial compression ratio, vertical bar yield strength, horizontal bar yield strength, web vertical reinforcement ratio, web horizontal reinforcement ratio, boundary region vertical reinforcement ratio, boundary region horizontal reinforcement ratio, sectional area ratio, sectional heigth thickness ratio, total section area, wall height, and section shape. ANN1 and ANN2 were normalized in interval of [0, 1] and [0.1, 0.9], respectively. The shear force of the RC shear walls was the output data for both models. The predictions of the shear bearing capacity of the RC shear walls by ANN models and code methods from GB50011 and ACI318 were compared. The results reveal that the developed models exhibit better prediction and generalization capacity for RC shear walls.