NEURAL-NETWORK-BASED APPROACH FOR PREDICTION OF THE FIRE RESISTANCE OF CENTRICALLY LOADED COMPOSITE COLUMNS

Abstract

The use of the neural-network-based approach, as an unconventional approach for solving complex civil engineering problems, has a huge significance in the modernization of the construction design processes. Worldwide studies show that artificial neural networks can be successfully used as prognostic model in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made. This paper presents some of the positive aspects of their application for determination the fire resistance of centrically loaded steel-concrete composite columns exposed to fire from all sides. The analyses were performed for three different types of composite columns: totally encased, partially encased and hollow steel sections filled with concrete. The influence of the shape, the cross sectional dimensions and the intensity of the axial force to the fire resistance of centrically loaded composite columns were analysed using the program FIRE. The results of the performed numerical analyses were used as input parameters for training the neural network model which is capable for predicting the fire resistance of centrically loaded composite columns.