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An optimization method for the distance between exits of buildings considering uncertainties based on arbitrary polynomial chaos expansion

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  • Xie, Qimiao
  • Wang, Jinhui
  • Lu, Shouxiang
  • Hensen, Jan L.M.

Abstract

The distance between exits is an important design parameter in fire safety design of buildings. In order to find the optimal distance between exits under uncertainties with a low computational cost, the surrogate model (i.e. approximation model) of evacuation time is constructed by the arbitrary polynomial chaos expansion. Through a two-stage nested Monte Carlo simulation of this surrogate model, the optimal distance between exits under uncertainty is found efficiently. In order to demonstrate the proposed method, a single room with two exits is presented as a fire compartment and uncertainties of occupant density and child-occupant load ratio are also considered. In this case, the results showed that the optimal distance between exits changes with the level of probability of evacuation time, and there is a critical level of probability for the transition of the optimal value of the distance between exits. Furthermore, the traditional Monte Carlo simulation method is used to compare the accuracy of the surrogate model with the computer evacuation model FDS+Evac developed by the VTT Technical Research Center of Finland [1]. The results indicate that the proposed surrogate-based optimization method can achieve a similar accuracy with a much lower computational cost.

Suggested Citation

  • Xie, Qimiao & Wang, Jinhui & Lu, Shouxiang & Hensen, Jan L.M., 2016. "An optimization method for the distance between exits of buildings considering uncertainties based on arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 188-196.
    • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:188-196
    DOI: 10.1016/j.ress.2016.04.018
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    References listed on IDEAS

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    1. Lovreglio, Ruggiero & Ronchi, Enrico & Borri, Dino, 2014. "The validation of evacuation simulation models through the analysis of behavioural uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 166-174.
    2. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    3. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
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    Cited by:

    1. Wang, Xinjian & Liu, Zhengjiang & Loughney, Sean & Yang, Zaili & Wang, Yanfu & Wang, Jin, 2022. "Numerical analysis and staircase layout optimisation for a Ro-Ro passenger ship during emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Oladyshkin, Sergey & Nowak, Wolfgang, 2018. "Incomplete statistical information limits the utility of high-order polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 137-148.

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