- Title
- Multi-attribute fire safety evaluation of subway stations based on FANP – FGRA – Cloud model
- Creator
- Chai, Naijie; Zhou, Wenliang; Chen, Ziyu; Lodewijks, Gabriel; Zhao, Yingying
- Relation
- Tunnelling and Underground Space Technology Vol. 144, Issue February 2024, no. 105526
- Publisher Link
- http://dx.doi.org/10.1016/j.tust.2023.105526
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2024
- Description
- With the rapid development of subway network, the fire safety of subway stations is becoming outstandingly important in the operation process. But there seem to be few studies developing quantitative models in the existing approaches. To this end, this study aims to develop a multi-attribute hybrid method by combining Fuzzy Analytic Network Process (FANP), and Fuzzy Grey Relational Analysis (FGRA) with Cloud model to evaluate fire safety of them. This research comprehensively sorts out and identifies high-frequency hazard factors associated with fire accidents at stations from personal, equipment, environmental and management aspects, and simultaneously explores dependencies and interactions among them. Seven typical transfer stations of Changsha subway network in China are studied by using the proposed approach, and the results show that: (1) the fire safety order from the best to the worst determined is FBH (A2), SWGY (A7), CSRS (A6), YWZ (A1), HTL (A5), HJT (A4) and WYGC (A3); (2) through comparing the results obtained by the proposed method with that determined fuzzy TOPSIS and fuzzy sum weighted method, it is proved that the method developed in this study is reliable and effective; and (3) sensitivity analysis indicates that the ranking order of stations is influenced by the fluctuation of criterion and sub-criterion weights.
- Subject
- fire safety of subway station; Fuzzy Analytic Network Process (FANP); Fuzzy Grey Relational Analysis (FGRA); cloud model; quantitative evaluation; SDG 17; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1497927
- Identifier
- uon:54450
- Identifier
- ISSN:0886-7798
- Language
- eng
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