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논문 기본 정보

자료유형
학술저널
저자정보
최진아 (경기대학교) 이준성 (경기대학교) 김종현 (경기대학교)
저널정보
아태인문사회융합기술교류학회 아시아태평양융합연구교류논문지 아시아태평양융합연구교류논문지 제9권 제5호
발행연도
2023.5
수록면
41 - 53 (13page)
DOI
http://dx.doi.org/10.47116/apjcri.2023.05.04

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A deterministic analysis that assumes a physical quantity including variance as a representative value, such as an average value, is inevitably conservative when compared to a phenomenon that actually causes damage. In addition, if there are not enough data available, conservatively selected values may not predict actual damage. Probabilist evaluation techniques can be applied efficiently because they predict unmeasured data through statistical analysis of existing measured data even if there is no actual measured data. Therefore, this paper aims to confirm the validity of the method through a case study using the probabilistic analysis method. The currently used scenario for predicting the risk of a tunnel fire accident contains many uncertainties such as the tunnel length, passenger occupancy rate, and the propagation time of a train fire, so conservative assumptions are used. In the existing studies, it is difficult to quantitatively evaluate each variable, so sufficient research has not been conducted, especially in terms of not clearly presenting the risk assessment results for the influencing factors because the uncertainty of the data is not taken into account. However, it is judged effectively to represent most of the variables predicting the survival rate of passengers in a tunnel fire as a probability distribution including variance. This study aims to investigate the risk assessment of tunnel fires in railway systems. In this regard, various accident scenarios were assumed by using a probabilistic approach to consider the uncertainty in the event of a fire, and then the survival rate of passengers under conditions affecting the accident scenario was predicted. The effects of factors on survival of passengers in tunnel fires were also analyzed. As a result, it was found that smoke propagation speed, fire detection time, and emergency support arrival time have a great effect on the passenger’s survival rate.

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