Deep neural network applied to joint shear strength for exterior RC beam-column joints affected by cyclic loadings

Abstract

The impact of reinforced concrete beam-column joints on the shear strength of a building under cyclic loading depends on the types of joints applied. This study considers models of the uniaxial and biaxial joint shear strength of exterior beam-column joints. Prediction models of the uniaxial shear strength under uniaxial cyclic loading based on ACI 352, ASCE 41, and gene expression programming (GEP) have been developed. The ACI 352, ASCE 41, and GEP formulas have the potential to achieve improved results. This study considers a means by which to improve the results of previous models through a proposed deep neural network (DNN) model with three hidden layers among the artificial neural network structures. The R-squared value and mean absolute error determined through this DNN model are 97.94% and 34.13% for the uniaxial model and 98.28% and 2.70% for the biaxial model, respectively. These results indicate that the DNN model is more suitable than the ACI 352, ASCE 41, and GEP models for joint shear strength predictions. © 2021 Institution of Structural Engineers