Original scientific paper

https://doi.org/10.17559/TV-20150223215657

Neural-network-based approach for prediction of the fire resistance of centrically loaded composite columns

Marijana Lazarevska orcid.org/0000-0002-9090-071X ; Faculty of Civil Engineering, Partizanski odredi 24, 1000 Skopje, Macedonia
Meri Cvetkovska ; Faculty of Civil Engineering, Partizanski odredi 24, 1000 Skopje, Macedonia
Ana Trombeva Gavriloska ; Faculty of Architecture, Partizanski odredi 24, 1000 Skopje, Macedonia
Miloš Knežević orcid.org/0000-0002-4952-9699 ; Faculty of Civil Engineering, Cetinjski put b.b., Podgorica, Montenegro
Milivoje Milanović orcid.org/0000-0003-3850-6480 ; State University of Novi Pazar, Serbia

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.

Keywords

civil engineering; composite columns; fire resistance; neural network

Hrčak ID:

167512

URI

https://hrcak.srce.hr/167512

Publication date:

13.10.2016.

Article data in other languages: croatian

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