# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 2 Fire Simulation for Multiplex Building
### Description of Fire Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Chemical Formula** & **CO Yield,** & **Soot Yield,** \\
**(Fuel Type)** & & **Yco (g/g)** & **Ysoot (g/g)** \\ \hline Ethanol & C\({}_{2}\)H\({}_{5}\)OH & 0.008 & - \\ Kerosene & C\({}_{4}\)H\({}_{30}\) & 0.012 & 0.042 \\ Polystyrene, GM47 & CH\({}_{1.1}\) & 0.06 & 0.18 \\ Polyurethane foams, GM27 & CH\({}_{1.7}\) & 0.042 & 0.198 \\ Wood (red oak) & C\({}_{1.7}\)H\({}_{0.72}\)O\({}_{0.001}\) & 0.004 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties of fuels. Figure 4: Heat release rates for \(\text{t}^{2}\) fires. Figure 3: Plan of the multiplex building  
building. Instead, as a worst-case scenario, the FDS analysis was performed considering only natural ventilation conditions through openings, i.e., doors and windows. Note that the building for training the ANN has 42 openings (windows and doors), which is 17.4% of the opening ratio, i.e., the ratio of total opening area to outer wall perimetric area. kerosene: 2.0 m, polystyrene: 0.9 m, polyurethane: 0.7 m, wood: 3.8 m) with respect to the fuel types. In addition, the time at which the temperature at the fire compartment reached 60 \({}^{\circ}\)C ranged from 207 to 225 s (ethanol: 225 s, kerosene: 207 s, polystyrene: 223 s, polyurethane: 221 s, wood: 217 s). For the concentration of oxygen according to the temperature shown in Figure 6b, when the temperature at the fire compartment reached 60 \({}^{\circ}\)C, the concentration of oxygen ranged from 20.1% to 20.7%, indicating that the fuel type has no significant effect on oxygen concentration. In addition, as shown in Figure 6c, the concentration of carbon monoxide ranged from 31 to 140 ppm (ethanol: 140 ppm, kerosene: 87 ppm, polystyrene: 129 ppm, polyurethane: 101 ppm, wood: 31 ppm) with respect to the fuel types, and the concentration of carbon dioxide showed a similar increasing tendency regardless of the fuel type, as shown in Figure 6d. Figure 7 shows the visibility and concentrations of oxygen, carbon monoxide, and carbon dioxide at the nonfire compartment, depending on the temperature and distance from the fire source. Since the analysis results did not show a large difference according to the type of fuel, the analysis result obtained using polyurethane for fuels was used as a representative case. As shown in Figure 7a, the visibility according to the temperature tended to decrease significantly regardless of the distance from the fire location because the smoke spread rapidly after a fire occurred. In addition, the concentration of oxygen depending on the temperature decreased rapidly as the distance from the fire location decreased, as shown in Figure 7b. Meanwhile, in the case of the carbon monoxide and carbon dioxide concentrations with respect to the temperature, the slope of the line on the graph tended to increase with the increasing distance from the fire location, as shown in Figure 7c,d. Figure 6: FDS analysis results with respect to fuel types at the fire compartment. In this study, the ASET was evaluated in accordance with the tenability criteria shown in Table 2, based on the results of the simulation on the fire compartment and nonfire compartment, and DBs were built in this connection. Figures 8 and 9 show the fire simulation results of fire compartment and nonfire compartment. In the NFPA, it is stipulated that the visibility should be larger than 5 m, the temperature less than 60 \({}^{\circ}\)C, CO concentration less than 1400 ppm, O\({}_{2}\) greater than 15%, and CO\({}_{2}\) less than 5%, in order to secure egress of occupants under fire. Therefore, the values in Figures 8 and 9 were normalized in accordance with the tenability criteria of the NFPA, based on which DB groups were classified for ANN training. Tables 4 and 5 summarize the slope values derived through the linear regression analysis for each graph. As shown in Figure 8 and Table 4, the DB group for ANN training was not divided into groups for the fire compartment.