This study developed a fire evacuation simulation model for a six-level underground station to evaluate evacuation efficiency under both dynamic and static conditions, including structural damage, smoke propagation, and real-time crowd congestion. Two representative pathfinding algorithms, Dijkstra’s and A*, were applied to analyze evacuation performance across eight fire scenarios occurring at different locations within the station. When only static factors were considered, both algorithms yielded identical maximum evacuation times, indicating comparable performance. However, the A* algorithm exhibited a significantly shorter computation time than Dijkstra’s, demonstrating higher operational efficiency. When dynamic variables such as real-time congestion and smoke-induced visibility reduction were introduced, the maximum evacuation times varied irregularly between the two algorithms. This outcome suggests that, under dynamic fire conditions, route guidance based solely on current information rather than predictive modeling may lead to suboptimal evacuation outcomes. Therefore, this study emphasizes the importance of establishing a predictive disaster management system capable of forecasting fire and smoke propagation, as well as a centralized control system that can dynamically distribute evacuees to enhance evacuation efficiency in deep underground stations.

This study proposes a novel model to quantitatively evaluate functionality loss in railway network systems during earthquakes and assesses its applicability to a hypothetical railway network system. The model combines seismic fragility functions and restoration curves to assess functionality loss, deriving a time-dependent recovery function to propose a functionality loss model based on earthquake magnitude. The proposed model uses a hypothetical railway network to calculate the overall functionality loss of the network under various earthquake scenarios. The hypothetical railway network was designed with three lines, allowing different routes to remain operational depending on the damaged sections and increasing the diversity of network impact scenarios based on the functionality loss. This model provides a framework for analyzing the functionality loss and recovery processes of railway networks during seismic events and assessing the socioeconomic impacts of earthquakes.

Herein, the Dijkstra algorithm was used to develop a model that considers evacuee congestion and derives an optimal evacuation route in underground structures in the event of an earthquake. The ground conditions and seismic intensities were varied, and the evacuation route was analyzed for four cases. The damage index for each underground structure due to an earthquake was determined considering the ground conditions and structure depth, and the evacuation speed reduction was evaluated as a function of the damage index. A congestion coefficient was applied when the evacuation capacity exceeded the threshold to reflect the evacuation speed reduction due to increased congestion in the same evacuation route. The evacuation route in some sections changed when congestion was considered, and the final evacuation time increased significantly when the congestion coefficient was applied. When the evacuation capacity at each node exceeded the threshold, the 1/3 value was applied as the congestion coefficient to evacuation velocity. When the original evacuation route was used after applying the congestion coefficient, the evacuation time increased by up to 220%. However, the evacuation time can be reduced by applying an alternative route that considers congestion. When an alternative route derived from considering congestion was used, the evacuation time decreased by up to 45% compared to that when the original route was used, and the time required decreased by up to 840 s. Hence, the reduction in evacuation speed due to evacuee congestion must be considered to derive alternative, optimal evacuation routes in the event of a disaster. In addition, evacuation routes should account for the location of evacuees using technologies such as real-time indoor positioning to consider the congestion level of evacuees.

Data halls within data centers are high-risk and high-value facilities. However, ensuring fire safety has proven challenging owing to the performance limitations of gas-suppression systems. In this study, the fire-suppression performance and design validity of a single-interlock pre-action sprinkler system were quantitatively assessed via a fire dynamics simulator (FDS) and SprinkCALC simulations. The FDS analysis revealed that, whereas the gas-suppression system showed limited effectiveness in controlling the HRR and reducing temperature owing to its one-time discharge, the sprinkler system demonstrated a meaningful effect on temperature reduction through its continuous cooling action. However, the control effect on the HRR was minimal, thus indicating that the sprinkler system is limited in verifying its effectiveness for overall fire control. Furthermore, the SprinkCALC analysis confirmed that the pre-action sprinkler system satisfied both the NFPA-required discharge time of within 50 s and the design density criteria. The results of this study suggest the potential applicability of sprinkler systems as a future fire-protection strategy for data centers.

This study proposes a scenario-based framework for quantifying seismic-induced transportation volume loss and economic loss in railway networks by integrating component-level seismic vulnerability assessment with system-level transportation demand analysis. Numerical simulations were performed on an actual regional railway network, comprising major high-speed and conventional lines on the Korean Peninsula and constructed using origin–destination (OD) matrix-based passenger and freight data. Seismic fragility curves and restoration functions were combined to derive seismic functionality loss curves for key structural components, including bridges, embankments, and tunnels. A deterministic earthquake scenario was applied, and spatial ground motion intensity was estimated using regional attenuation relationships and site amplification factors. The resulting functionality degradation was propagated to the network level to quantify transportation volume loss and cumulative economic loss over a 100-day restoration period. Economic loss in this study refers to the direct transportation revenue loss caused by the degradation of network functionality; repair and replacement costs and indirect economic losses are not included. The results indicate that passenger transportation volume on the high-speed line decreased by approximately 60%, resulting in a cumulative economic loss of approximately KRW 391 billion, while freight-oriented conventional lines exhibited transportation losses exceeding 50%. The proposed framework demonstrates how localized structural damage can propagate into system-wide disruption and provides a quantitative basis for restoration prioritization, resource allocation, and seismic risk-informed decision-making for railway infrastructure systems.