Shear Capacity Prediction of Composite Beam–Column Joints: A Review of Analytical, Numerical, and Data-Driven Methods

Abstract

Composite beam–column joints play a critical role in the seismic performance of moment-resisting frame systems, since they transfer axial loads, bending moments, and shear forces between beams and columns under extremely nonlinear loading circumstances. Therefore, accurate estimation of these joints' shear capacity is crucial for trustworthy seismic assessment, design, and strengthening choices. Because it depends on the interaction between concrete, reinforcing bars, transverse reinforcement, embedded steel sections, bond behavior, joint geometry, and loading history, the shear-resisting mechanism in composite and steel-reinforced concrete (SRC) beam–column joints is more complicated than that of conventional reinforced concrete joints. The primary analytical, empirical, numerical, and computational techniques for estimating the shear capacity of composite beam-column joints are reviewed in this paper, with a focus on SRC joint systems. The reviewed methods are classified into four main categories: component-based analytical models, code-based and empirical approaches, finite element modelling approaches, and artificial neural network techniques using Java Neural Network Simulator (JavaNNS). Within the empirical and semi-empirical category, particular attention is given to the methods proposed by Wei Liu and Jinqing Jia and by Cheng-Cheng Chen, which have been widely used to estimate the shear strength of SRC beam–column joints based on experimental observations and key mechanical parameters.