Application of Artificial Neural Networks for Prognostic Modeling of Fire Resistance of Reinforced Concrete Pillars

Abstract:

Artificial neural networks can be used for building prognostic models of various engineering problems. This paper presents an example of how we can predict the time of fire resistance based on the given experimental and numerical results. The analyses concerning the behavior of the reinforced-concrete construction elements during the standard fire, together with the basic theoretical information and detailed problem description, as well as the graphical curves for the fire resistance of the reinforced-concrete pillars, are given in the doctoral theses of Prof. Cvetkovska [3]. Using the concepts of artificial neural networks and the results of the performed numerical analyses as input parameters we made the prediction model for determination of the time of fire resistance of reinforced-concrete pillars. The neural network generated excellent results which will be presented further below in this paper.

• Periodical: Applied Mechanics and Materials (Volumes 148-149)
• Pages: 856-861
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.148-149.856
• Online since: December 2011
• Authors: Marijana Lazarevska, Milos Knezevic, Meri Cvetkovska
• Keywords: Artificial Neural Network (ANN), Fire Resistant, Prognostic Model, Reinforced-Concrete Pillars

References

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[2] Knežević M.: Risk management of civil engineering projects, doctoral dissertation, Civil Engineering Faculty of University in Belgrade, Serbia (2005).

[3] Cvetkovska M.: Nonlinear stree strain behavior of RC elements and frames exposed to fires, doctoral dissertation, Civil Engineering Faculty in Skopje, Sts Cyril and Methodius in Macedonia (2002).

[4] Knežević M. and Radomir Z.: Neural networks – application for usage of prognostic model of the experimental research for thin reinforced-concrete columns, scientific research work, Materials and constructions (2008).

[5] Kerzner H.: Project management, a systems approach to planning, scheduling and controlling, Division of Business Administration Baldwin-Wallace College Berrea, Ohio, United States of America, 2005, ISBN 978-0-471-74187-9.