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APPLICATION OF ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM TO

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					                                                                                             CHT-04-212



    APPLICATION OF ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
       TO OPTIMIZATION OF DESIGN PARAMETERS IN COMPARTMENT FIRE
†
 *Richard K.K. Yuen, *Eric W.M. Lee, **W.K. Kwok, *Sherman C.P. Cheung and ***G. H. Yeoh
                      *Fire Safety and Disaster Prevention Research Group
  (Department of Building and Construction, City University of Hong Kong, 83 Tat Chee Avenue,
                   Kowloon Tong, Kowloon, Hong Kong (SAR), P.R. China)
                             **Hutchison Whampoo Properties Ltd.
 (3/F One Harbourfront, 18 Tak Fung Street, Hunghom, Kowloon, Hong Kong (SAR), P.R. China)
***Australian Nuclear Science and Technology Organisation, PMB 1, Menai NSW 2234, Australia
         †
             Correspondence author: Fax: +852 2784 4655       Email: bckkyuen@cityu.edu.hk

ABSTRACT The Computational Fluid Dynamics (CFD) techniques are currently widely adopted to
simulate the behaviour of fire. The major shortcoming of the CFD is the requirements of extensive
computer storage and lengthy computational time. In actual applications, although comprehensive field
information of velocities, temperature, pressure, fraction of different constitutes etc. can be obtained
from the CFD simulation, the user may be only interested in few important parameters which index the
performance of the compartment design in the event of fire. Height of thermal interface (HTI) is one of
the key indices. It is the average height above the floor level inside the fire compartment at which the
temperature gradient is the highest. In practice, the fire compartment is considered untenable when the
HTI descends lower than the respiratory level of the occupants. In the course of fire system design
optimization, if the resultant HTI of a fire compartment design evaluated by the CFD is too low,
another set of the design parameters (e.g. width of the door opening) are required to be tried. This trial
and error exercise continues until a close optimum set of the design parameter achieved. This approach
is theoretically feasible but requires lengthy computational time. This paper proposes to apply Artificial
Neural Network (ANN) approach as a fast alternative to the CFD models to simulate the behaviour of
the compartment fire. A novel ANN model denoted as GRNNFA has been developed particular for fire
studies. It is a hybrid ANN model combining the General Regression Neural Network (GRNN) and the
Fuzzy ART (FA). The GRNNFA model owns the features of incremental growth of network structure,
stable learning and removal of the noise embedded in the experimental fire data. It has been employed
to establish a system response surface based on the knowledge of the available training samples. Since
the available training samples may not be sufficient to describe the system behaviour especially for fire
data, it is proposed to acquire extra knowledge of the system from human expert knowledge. The
human expert intervened network training was developed to remedy the established system response
surface. After the transformation of the remedied system response surface to the problem domain,
Genetic Algorithm (GA) is applied to evaluate the close optimum set of the design parameters.

				
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