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Predicting the behavior of welded angle connections in fire using artificial neural network
This paper aims to predicting the behavior of welded angle connections (moment-rotation-temperature) in fire using artificial neural network 10.
An artificial neural networking model is described to predict the moment-rotation response of semi-rigid beam-to-column joints at elevated temperature.
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.
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.
Predicting the behavior of welded angle connections in fire using artificial neural network
This paper aims to predicting the behavior of welded angle connections (moment-rotation-temperature) in fire using artificial neural network 10.
An artificial neural networking model is described to predict the moment-rotation response of semi-rigid beam-to-column joints at elevated temperature.
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.
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.
Predicting the behavior of welded angle connections in fire using artificial neural network
Saedi Daryan, Amir (author) / Yahyai, Mahmood (author)
Journal of Structural Fire Engineering ; 9 ; 28-52
2017-07-14
1 pages
Article (Journal)
Electronic Resource
English
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