Abstract
Computational fluid dynamics (CFD) techniques are currently widely adopted to simulate the behaviour of fire but it requires extensive computer storage and lengthy computational time. Using CFD in the course of building design optimization is theoretically feasible but requires lengthy computational time. This paper proposes the application of an artificial neural network (ANN) approach as a quick alternative to CFD models. A novel ANN model that is denoted as GRNNFA has been developed specifically for fire studies. As the available training samples may not be sufficient to describe system behaviour, especially for fire data, additional knowledge of the system is acquired from a human expert. The expert intervention network training is developed to remedy the established system response surface. A genetic algorithm is applied to evaluate the close optimum set of the design parameters.
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Lee, E.W.M., Lau, P.C., Yuen, K.K.Y. (2006). Application of Artificial Neural Network to Building Compartment Design for Fire Safety. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_32
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DOI: https://doi.org/10.1007/11875581_32
Publisher Name: Springer, Berlin, Heidelberg
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