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Predicting the Behaviour of Semi-rigid Joints in Fire Using an Artificial Neural Network
Khalifa S. Al-Jabri,Saleh M. Al-Alawi 한국강구조학회 2007 International Journal of Steel Structures Vol.7 No.3
In this paper, we describe an artificial neural networking (ANN) model developed to predict the moment-rotation responseof semi-rigid beam-to-column joints at elevated temperature. Five types of beam-to-column joints, which represent typicaljoints used in construction, were modelled. Three flush end-plate bare-steel joints, one flexible end-plate bare-stel joint andtwo flexible end-plate composite joints were considered. The aplied moment and joint’s temperatures were used as inputeused for training and testing and validating the neural network models. The model’s predicted values were compared with actualtest results. The results indicate that the models can predict the momentrotationtemperature behaviour of semi-rigid beam-to-column joints with very high accuracy. The developed model can be modified easily to investigate other parameters thatinfluence the performance of joints in fire.