To address the significant problems of high fire risk and low evacuation efficiency in large and complex medical buildings, this study uses Ezhou Hospital as the empirical object to construct a multi-dimensional threat and risk assessment and fire evacuation dynamic coupling model and proposes a systematic optimization scheme to improve personnel evacuation safety. This study proposes an innovative full-chain analysis framework of “threat and risk assessment-dynamic coupling-multi-strategy optimization”. The specific methods employed include the following: (1) Using the probabilistic threat and risk assessment (PRA) method and the risk index (RII) method to identify the most unfavorable scenarios where the fire source is located in the outpatient hall (risk value C2 = 9.86). (2) Combining PyroSim and Pathfinder to construct a dynamic coupling model of fire smoke diffusion and personnel evacuation. Multiple groups, such as patients with mobility problems and rescue personnel, are added to address the limitations of traditional single-factor simulations. (3) Considering the failure of fire shutters, a two-stage optimization strategy is proposed for when the number of personnel is at its peak: the evacuation time is shortened by 23% by using internal intelligent guidance to shunt the congestion node crowd, and the addition of external fire ladders forms a multi-channel coordinated evacuation that further reduces the total evacuation time from 1780 s to 1266 s and improves the efficiency by 29%. The results show that the coupled multi-path coordination strategy and three-dimensional rescue facilities can significantly reduce the bottleneck associated with a single channel. This study provides a multi-dimensional dynamic evaluation framework and comprehensive optimization paradigm for the design of the evacuation of high-rise medical buildings and has important theoretical and technical reference values for improving the fire safety performance of public buildings and the intelligence of emergency management.

Due to the complexity of the architectural structure within comprehensive teaching buildings and the diversity of the student population, these buildings face more intricate fire safety challenges than ordinary teaching buildings. Current research primarily focuses on the analysis of single-building structures or individual factors, lacking an examination of the mechanism of multiple factors on emergency evacuation. Therefore, this study takes a comprehensive teaching building with a complex structure as a case study and, considering the behavioral characteristics of university personnel, conducts simulations using Pathfinder software. The model evaluates the effectiveness of pedestrian flow, vertical personnel distribution, horizontal functional zoning, priority ranking adjustments, and combination strategies on evacuation, as well as the impact of psychological factors on evacuation efficiency, providing a comparative analysis of the influence of each factor on evacuation. The results indicate that controlling the number of people in classrooms at the same time to reduce pedestrian flow can effectively shorten evacuation time, improving evacuation efficiency by 17.63%. The reasonable optimization of functional zoning and priority ranking can also effectively reduce evacuation time. In cases where there is high personnel density on upper floors and the teaching building’s functional zoning is unreasonable, the optimization effect of combination strategies is particularly significant, improving evacuation efficiency by 23.94%. Under panic conditions, leaders can effectively improve evacuation efficiency, and their role becomes increasingly significant as the level of panic rises. By considering the impact of various factors on evacuation, this research aims to enhance the evacuation efficiency of teaching buildings. The findings provide a scientific basis for emergency evacuation in complex buildings like teaching facilities.

Fire safety in high-rise talent apartments, which is vital for safeguarding strategic human resources, was investigated to enhance evacuation resilience. A coupled fire-evacuation model was established using PyroSim and Pathfinder. This study analyzed multi-factor management strategies, including occupant vertical distribution, evacuation speed, evacuation priority settings, panic psychology, and guide intervention. Additionally, an Artificial Neural Network (ANN) model was developed using simulation data obtained under non-panic conditions to predict evacuation time and explore intelligent algorithms. Results show that the evacuation stairwells are the primary bottlenecks. Panic psychology significantly reduces evacuation efficiency, with severe panic increasing total evacuation time by up to 71.1%. The combined strategy CS4, integrating Pyramidal Vertical Distribution (VD7) and Organized Segmented Speed Control (ES6), reduced evacuation time by 17.42%. Guide intervention effectively mitigates the negative impact of panic. The ANN model achieved a coefficient of determination (R²) of 0.8695, confirming its predictive capability. Visibility was identified as the key parameter determining the Available Safe Egress Time (ASET). This study demonstrates that an integrated “hard–soft combination” strategy, complemented by intelligent modeling, offers an effective approach to building a resilient evacuation system for high-rise talent apartments.