Application of Artificial Neural Network to Building Compartment Design for Fire Safety

  • Eric Wai Ming Lee
  • Po Chi Lau
  • Kitty Kit Yan Yuen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


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.


Artificial Neural Network Computational Fluid Dynamics Artificial Neural Network Model Computational Fluid Dynamics Simulation Fire Safety 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eric Wai Ming Lee
    • 1
  • Po Chi Lau
    • 2
  • Kitty Kit Yan Yuen
    • 1
  1. 1.Fire Safety and Disaster Prevention Research Group, Department of Building and, ConstructionCity University of Hong KongKowloon Tong, Hong Kong (SAR)People of Republic of China
  2. 2.Asian Institute of Intelligent Buildings, c/o Department of Building and ConstructionCity University of Hong KongKowloon Tong, Hong Kong (SAR)People of Republic of China

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