Discovering worst fire scenarios in subway stations: A simulation approach

Abstract This paper develops a systematic hybrid approach that integrates Available Safe Egress Time (ASET), Required Safe Egress Time (RSET), numerical simulation, and Multi-Attribute Decision Analysis (MADA) to support fire safety risk assessment and to discover the worst fire scenarios for improving evacuation efficiency. Three factors, namely heat release rate, fire location, and occupants, are used to identify the most likely fire scenarios with a high-potential risk. The main classes of hazards yielded by fire, including heat (temperature), toxic gases (carbonic oxide), and smoke obscuration (visibility), are employed as untenability criteria for the estimation of ASET in the numerical simulation. A more comprehensive indicator, SITotal, is proposed to quantify the magnitude of the overall safety risk of a building fire, in order to fully consider the fire escape performance in different evacuation routes. One realistic subway station located at the Wuhan Metro System in China is utilized as a case to testify the applicability and feasibility of the proposed approach in this research. Result indicate that (i) among the identified four most likely fire scenarios, Scenario IV, where the fire is located at the exit of Sair I at the hall floor of the station with a heat release rate of 3 MW/m2, is identified to be the worst fire scenario with an associated lowest value of SITotal; (ii) the fire release rate plays a very significant role in the magnitude of the fire safety risk, as a 50% increase of the fire release rate can lead to a rough 36% decrease of SITotal; and (iii) exits should be regarded as super bottlenecks with significant importance during the fire escape process, and much more attention should be paid to those bottlenecks in the possible evacuation routes. The simulation models developed in this research are further validated by the observed results from the field test and experiment. This research contributes: (a) to the body of knowledge by providing an improved ASET/RSET approach that is capable of taking numerical factors (i.e., fire, building, and human features) into account to assess the safety risk of fire conditions in a three-dimensional environment; and (ii) to the state of practice by providing a more accurate data-driven solution for the perception and discovery of the worst fire scenarios.