2013
DOI: 10.1177/1042391512470578
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Treatment of evacuation time uncertainty using polynomial chaos expansion

Abstract: In order to deal with uncertainties in evacuation time associated with the uncertainty in input parameters at a reasonable computational cost, a probabilistic method based on polynomial chaos expansion is proposed that combines evacuation models with Latin hypercube sampling. Evacuation models enable the prediction of evacuation time; polynomial chaos expansion is used to construct a surrogate model of evacuation time; Latin hypercube sampling is adopted as post-processing of the surrogate model to predict num… Show more

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Cited by 8 publications
(2 citation statements)
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“…However, the uncertainty of evacuation time is affected by many factors such as occupancy type, exit width, and the characteristics of evacuees, which makes safety factors random. Thus, the effect of exit width on uncertainty factor [26], which is defined as the ratio of evacuation time under uncertainty to the base case evacuation time, is investigated, as shown in Fig. 6.…”
Section: Fig 5 Evacuation Time T E Under Uncertainty Versus Exit Widthmentioning
confidence: 99%
“…However, the uncertainty of evacuation time is affected by many factors such as occupancy type, exit width, and the characteristics of evacuees, which makes safety factors random. Thus, the effect of exit width on uncertainty factor [26], which is defined as the ratio of evacuation time under uncertainty to the base case evacuation time, is investigated, as shown in Fig. 6.…”
Section: Fig 5 Evacuation Time T E Under Uncertainty Versus Exit Widthmentioning
confidence: 99%
“…Moreover, Averill et al [7] employed a Modified-Markov Modeling approach to propose a grid-based evacuation model, which can be used to deal with uncertainty in the number of occupants. Additionally, Xie et al [8] utilized the polynomial chaos expansion to solve uncertainties in input parameters with the purpose of reducing the computational cost of uncertainty analysis of evacuation time. Besides, in order to address the randomness of occupant characteristics, distributions of stochastic variables (e.g.…”
Section: Introductionmentioning
confidence: 99%