Predicting the behavior of welded angle connections in fire using artificial neural network

Abstract

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.

Citation

Saedi Daryan, A. and Yahyai, M. (2018), "Predicting the behavior of welded angle connections in fire using artificial neural network", Journal of Structural Fire Engineering, Vol. 9 No. 1, pp. 28-52. https://doi.org/10.1108/JSFE-07-2016-0011