Real-time egress model for multiplex buildings under fire based on artificial neural network

Khaliunaa Darkhanbat, Inwook Heo, Sun Jin Han, Hae Chang Cho, Kang Su Kim

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


When fire occurs in a large multiplex building, the direction of smoke and flames is often similar to that of the evacuation of building occupants. This causes evacuation bottlenecks in a specific compartment, especially when the occupant density is very high, which unfortunately often leads to many fatalities and injuries. Thus, the development of an egress model that can ensure the safe evacuation of occupants is required to minimize the number of casualties. In this study, the correlations between fire temperature with visibility and toxic gas concentration were investigated through a fire simulation on a multiplex building, from which databases for training of artificial neural networks (ANN) were created. Based on this, an ANN model that can predict the available safe egress time was developed, and it estimated the available safe egress time (ASET) very accurately. In addition, an egress model that can guide rapid and safe evacuation routes for occupants was proposed, and the rationality of the proposed model was verified in detail through an application example. The proposed model provided the optimal evacuation route with the longest margin of safety in consideration of both ASET and the movement time of occupants under fire.

Original languageEnglish
Article number6337
JournalApplied Sciences (Switzerland)
Issue number14
StatePublished - 2 Jul 2021


  • Artificial neural network (ANN)
  • Available safe egress time (ASET)
  • Egress model
  • Fire
  • Multiplex building


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