Experimental and numerical analysis of beam to column joints in steel structures

Abstract

The behaviors such as extreme non-elastic response, constant changes in roughness and resistance, as well as formability under extreme loads such as earthquakes are the primary challenges in the modeling of beam-to-column connections. In this research, two modeling methods including mechanical and neural network methods have been presented in order to model the complex hysteresis behavior of beam-to-column connections with flange plate. First, the component-based mechanical model will be introduced in which every source of transformation has been shown only with geometrical and material properties. This is followed by the investigation of a neural network method for direct extraction of information out of experimental data. For the validation of behavioral curves as well as training of the neural network, the experiments were carried out on samples with real dimensions of beam-to-column connections with flange plate in the laboratory. At the end, the combinational modeling framework is presented. The comparisons reveal that the combinational modeling is able to display the complex narrowed hysteresis behavior of the beam-to-column connections with flange plate. This model has also been successfully employed for the prediction of the behavior of a newly designed connection.