TY  - JOUR
PY  - 2018//
TI  - Predicting the behavior of welded angle connections in fire using artificial neural network
JO  - Journal of structural fire engineering
A1  - Daryan, Amir Saedi
A1  - Yahyai, Mahmood
SP  - 28
EP  - 52
VL  - 9
IS  - 1
N2  - PURPOSE        This paper aims to predicting the behavior of welded angle connections (moment-rotation-temperature) in fire using artificial neural network 10.       Design/methodology/approach        An artificial neural networking model is described to predict the moment-rotation response of semi-rigid beam-to-column joints at elevated temperature.       Findings        Data from 47 experimental fire tests and verified finite element model are used for training and testing and validating the neural network models. The model's predicted values are compared with actual test results. The results indicate that the models can predict the moment-rotation-temperature behavior of semi-rigid beam-to-column joints with very high accuracy. The developed model can be modified easily to investigate other parameters that influence the performance of joints in fire.       Originality/value        The results indicate that the models can predict the moment-rotation-temperature behavior of semi-rigid beam-to-column joints with very high accuracy. The developed model can be modified easily to investigate other parameters that influence the performance of joints in fire.     Keywords:      Fire, Artificial neural network, Elevated temperature, Moment-rotation, Temperature-rotation, Welded angle connections    © Emerald Publishing Limited 2018<p />  <p>Language: en</p>
LA  - en
SN  - 2040-2317
UR  - http://dx.doi.org/10.1108/JSFE-07-2016-0011
ID  - ref1
ER  -