Plastic hinge modeling of reinforced concrete Beam-Column joints using artificial neural networks

Abstract

Beam-column joints play a critical role in transferring forces between beam and column elements and maintaining structural integrity during severe loading. While the nonlinear behaviors of beams and columns are commonly modeled in global frame analyses through the use of plastic hinges, the behavior of joints is often omitted through the use of rigid end offsets. The objective of this study is to develop an artificial neural network and derive the plastic hinge curves required for modeling reinforced concrete beam-column joints in global frame analyses. In pursuit of this objective, a feed-forward artificial neural network (FFNN) is developed to predict the shear strengths of beam-column joints. A comprehensive dataset of 598 experimental joint specimens is compiled from 153 previously published research studies. The 555 data points, which passed the exploratory data analysis, are used to train, test, and validate the proposed network for applicability to a wide range of input variables and joint configurations. The accuracy and reliability of the proposed FFNN are evaluated using a comprehensive set of evaluation metrics in comparison with three existing networks from the literature. The proposed FFNN is used to derive the shear stress–strain and moment-rotation curves required for defining joint hinges in global frame analyses. In addition, a spreadsheet tool is developed to execute the network formulations, calculate the joint shear strength, and derive the joint hinge curves for the use of engineers and researchers.