Predicting Shear Capacity of Panel Zone Using Neural Network and Genetic Algorithm

Abstract

Investigating the behavior of the box-shaped column panel zone has been one of the major concerns of scientists in the field. In the American Institute of Steel Construction the shear capacity of I-shaped cross- sections with low column thickness is calculated. This paper determines the shear capacity of panel zone in steel columns with box-shaped cross-sections by using artificial neural network (ANN) and genetic algorithm (GA). It also compares ABAQUS finite element software outputs and AISC relations. Therefore, neural networks were trained using parametric information obtained from 510 connection models in ABAQUS software. The results show that the predicted shear capacity of the NN and the GA in comparison with the AISC relations use a wide range of all effective parameters in the calculation of the shear capacity of panel zone. Therefore, the use of artificial intelligence can be a good choice. Finally, the GA, along with optimization of a mathematical relation, has been able to minimize the error in determining the shear capacity of panel zones of steel-based columns, even at high column thicknesses.