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Journal Article


Xie Q, Wang J, Wang P, Wang W, Jiao Y, Guo J. Procedia Eng. 2018; 211: 818-829.


(Copyright © 2018, Elsevier Publishing)






In prescriptive fire protection designs of buildings, exit location is generally designed with the constraint of the maximum travel distance. And the optimal exit location has not been given enough attention to in performance-based fire protection designs of buildings. However, in order to improve the reliability of successful evacuation, the optimal exit location for minimizing evacuation time should be studied with consideration of the uncertainty of initial occupant density. Due to the expensive computational cost, the surrogate-based optimization is proposed to search the optimal exit location, which combines the polynomial chaos expansion and genetic algorithm. The polynomial chaos expansion is used to construct the surrogate model of evacuation time and then genetic algorithm is applied to the polynomial chaos expansion of evacuation time to search the optimal exit location. In order to demonstrate the proposed method, a fire compartment of commercial buildings with two exits is presented. The results of this case study suggest that the optimal layout of two exits with the same size should be distributed symmetrically and the optimal exit location is different for different probability levels. When the probability level of successful evacuation is low with two narrow exits, the better method is merging two exits into be one exit around the center of the wall, which is also consistent with that determined by minimizing the maximum travel distance. However, when the probability level of successful evacuation is high with two narrow exits, the optimal location of two exits should be around two corners of the wall in this case. This proposed method can be used to find the suitable exit location under uncertain initial occupant density, which provides a reference for the performance-based design of building exits.

Language: en


evacuation time; exit location; fire protection design; optimization; uncertainty analysis


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