Beam-column joints are critical zones in reinforced concrete structures. The behavior of joints is very complex and governed by different mechanisms such as flexure, shear, and bond stress between the reinforcement and the concrete. Shear transfer mechanisms through the joint are one of the most important factors affecting the joint performance. Shear failure occurring in the joint can lead to severe damage and may result in the collapse of the structure. This thesis presents an investigation into the shear capacity of reinforced concrete beam-column joints. The performance is influenced by several key parameters. An analysis is carried out to simulate the behavior of the exterior beam-column joints subjected to monotonic loading and of interior joints subjected to reverse cyclic loading. The main parameters considered in this study are: joint shear reinforcement ratio, concrete compressive strength, beam tension longitudinal reinforcement ratio, joint aspect ratio, and column axial stress. The analysis is conducted using a database collected from different experimental programs in the literature. Based on this database, analytical models are created using two artificial intelligence approaches namely artificial neural networks (ANNs) and genetic algorithms (GAs). Evaluation of the existing formulae is conducted and the effect of each of the investigated parameters is stated and new formulae are proposed for the shear design of a reinforced concrete beam-column joint.
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