Interior Reinforced Concrete Beam-to-Column Joints Subjected to Cyclic Loading: Shear Strength Prediction using Gene Expression Programming

Abstract

An empirical model, which predicts the shear strength of interior reinforced concrete (RC) joints exposed to cyclic loading, is developed using gene expression programming (GEP). Five main parameters are used to develop the GEP model including concrete compressive strength, joint transverse reinforcement, beam reinforcement ratio, joint aspect ratio, and joint width. A large database including 160 data test points is used to test and train the GEP model. The GEP model is then evaluated using the coefficient of determination (R2). The formulation proposed by ACI-352 is used to predict the shear strength of RC joints. The R-squared values of the ACI and the GEP model are 66% and 87% respectively which indicates that the joint shear strength predicted using the GEP model is closer to the experimental results than that predicted using the ACI-352 formulation.

Keywords

• Gene expression programming
• joint shear strength
• interior joints
• cyclic loading
• ACI-352