Prediction of Behavior of Semi-rigid Composite Joints using Artificial Neural Networks

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Daoud, Osama M. A.
Mohammed, Rania Salih
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This paper describes an artificial neural network (ANN) model developed to predict the moment-rotation response of semi-rigid beam-to-column composite joints for the full range of loading. Experimental data from 35 tests including results of tests performed by first author, cover the most frequently used types of semi-rigid beam-to-column joints with a composite metal deck floor, were used for training, testing and validating the neural network models. The data were arranged in a format represents the ultimate moment and geometric and material properties of the joints as an input and the output is the corresponding joint rotation. Aback-propagation artificial neural network model is developed and used to predict the moment-rotation response of the composite joints. Results were compared to the available experimental tests results and to a simple semi-empirical method proposed by the American Society Civil Engineers. Results indicate that the developed models can predict the nonlinear moment-rotation behavior of semi-rigid joints with a high level of accuracy.
Composite Joints, Semi-rigid, Artificial Neural Network, Rotation.