Simulating size effect on shear strength of RC beams without stirrups using neural networks

An artificial neural network (ANN) model was developed using past experimental data on shear failure of slender RC beams without web reinforcements. The neural network model has five input nodes representing the concrete compressive strength (f′c), beam width (b), effective depth (d), shear span to depth ratio (a/d), longitudinal steel ratio (ρ), five hidden layer nodes and one output node representing the ultimate shear strength (vu=Vu/bd). The model gives reasonable predictions of the ultimate shear stress and can simulate the size effect on ultimate shear stress at diagonal tension failure. The ANN model performs well when compared with existing empirical, theoretical and design code equations. Through the parametric studies using the ANN model, the effects of various parameters such as f′c, d, ρ and a/d on the shear capacity of RC beams without web reinforcement was shown. This shows the versatility of ANNs in constructing relationships among multiple variables of complex physical processes using actual experimental data for training.