Data from: Performance-based Egress safety assessment of underground tunnels: Simulation and artificial neural network approaches

In this study, an Artificial Neural Network (ANN) model was proposed to
evaluate the egress safety of underground tunnels during fire. Fire
simulations were carried out using the Fire Dynamics Simulator (FDS) for
underground tunnels with a general cross-section, considering fire size as
a key variable. Additionally, egress simulations were performed using
Pathfinder, with the spacing of cross-passage and the width of fire doors
set as variables. Through this process, the available safe egress time
(ASET), required safe egress time (RSET), and the number of casualties
were derived for each variable, and the egress safety characteristics of
underground tunnels under various parameter combinations were analyzed in
detail. Based on the derived data, an Artificial Neural Network (ANN)
model was developed to derive the ASET, RSET, and survival rate in
underground tunnels during fire incidents. The proposed ANN model is
expected to efficiently evaluate the egress safety of underground tunnels
with general dimensions without the need to perform additional fire and
egress simulations.