# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network  
[PERSON]  
1  
[PERSON]  
2 Department of Architectural Engineering, University of Seoul, 163 Seoulspatipdea-ro, Dongdaemun-gu, Seoul 02504, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Architectural Engineering, University of Seoul, 163 Seoulspatipdea-ro, Dongdaemun-gu, Seoul 02504, Korea; [EMAIL_ADDRESS]  
[PERSON]  
3 Technology Center/R&D Manager, Dream Structural Engineers Co Ltd., 25 Seongsuilto 4-gil, Seongdong-gu, Seoul 04871, Korea; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Architectural Engineering and Smart City Interdisciplinary Major Program, University of Seoul, 163 Seoulspatipdea-ro, Dongdaemun-gu, Seoul 02504, Korea; [EMAIL_ADDRESS]  
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evaluations on movie theaters and provided the details about the size and location of openings to facilitate occupants' evacuation process, and [PERSON] et al. [7] conducted evacuation simulations on commercial office buildings and evaluated the ASET, which depends on the behavior of toxic gases. [PERSON] et al. [8] performed quantitative fire and explosion risk assessment based on the system dynamic (SD) simulation model. They considered a risk due to the use of liquefied petroleum gas (LPG) cylinders and reflected the human characteristics in their SD model. Recently, research has been actively done to develop evacuation facilities for efficient occupant evacuation in addition to the evaluation of fire safety [9; 10].  
In the field of engineering, studies have been conducted to solve problems that pose difficulties in numerical analysis due to the large uncertainty of input and output variables and complex mechanisms with the use of an artificial neural network (ANN) model [11; 12; 13]. The ANN is a regression analysis model that can examine complex nonlinear relationships between the input and output variables based on a database (DB), and has advantages in that it can minimize the error of output through the training process and provide output in a very short time when input data are given for the pre-trained model. If an ANN algorithm is utilized in the fire and evacuation analysis, the time required to derive the ASET can be reduced to about 1/100 s [14]. In the event of a fire in buildings, data obtained from temperature and toxic gas detection sensors can be used to estimate ASET in real time, and it is possible to provide the optimal evacuation route for the occupants.  
In this study, a fire simulation was conducted on the assumption of various fire scenarios for general multiplex buildings. The correlations between fire temperature and visibility, and between fire temperature and gas concentration, were investigated based on the simulation results, and DBs were built in those connections. In addition, an algorithm that can calculate real-time ASET in the case of a fire was developed using DB and ANN. The rationality of the proposed ANN model was verified by comparing the ASET derived using the proposed model with the ASET derived from the fire simulation. In addition, the ANN-based egress model that can provide optimal evacuation routes in the event of a fire was proposed, as shown in Figure 2, and an example was presented to verify the applicability of the proposed egress model.  
Figure 1: Multiplex buildings in fire.