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Title |
Evaluation on Fire Available Safe Egress Time of Commercial Buildings based on Artificial Neural Network
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Authors |
할리오나(Khaliunaa Darkhanbat) ; 허인욱(Inwook Heo) ; 최승호(Seung-Ho Choi) ; 김재현(Jae-Hyun Kim) ; 김강수(Kang Su Kim) |
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DOI |
https://doi.org/10.11112/jksmi.2021.25.6.111 |
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Abstract |
When a fire occurs in a commercial building, the evacuation route is complicated and the direction of smoke and flame is similar to that of the egress route of occupants, resulting in many casualties. Performance-based evacuation design for buildings is essential to minimize human casualties. In order to apply the performance-based evacuation design to buildings, it requires a complex fire simulation for each building, demanding a large amount of time and manpower. In order to supplement this, it would be very useful to develop an Available Safe Egress Time (ASET) prediction model that can rationally derive the ASET without performing a fire simulation. In this study, the correlations between fire temperature with visibility and toxic gas concentration were investigated through a fire simulation on a commercial building, from which databases for the training of artificial neural networks (ANN) were created. Based on this, an ANN model that can predict the available safe egress time was developed. In order to examine whether the proposed ANN model can be applied to other commercial buildings, it was applied to another commercial building, and the proposed model was found to estimate the available safe egress time of the commercial building very accurately.
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