The unique design and complex morphological characteristics of roof structures in the "purlin-type" ancient buildings of the Ming and Qing dynasties significantly influenced the spread of smoke. However, the existing fire detection systems used in ancient structures lack sufficient consideration of the impact of roof architecture on the placement of smoke detectors, which makes it challenging to realize early fire detection effectively and accurately. Therefore, further research on the response time of fire detectors considering purlin height is necessary to strengthen fire prevention and control in ancient buildings.

Through the job-site survey of several gable-roofed buildings in the Forbidden City, the existing smoke detectors were found to be primarily installed on the purlins below the ridge or both sides of the ceiling. However, the scientific basis for these installation positions remains unclear. Thus, this study selected typical gable-roofed buildings with a slope of 27.41° constructed during the Ming and Qing Dynasties as the research object. Fire dynamics simulator was utilized for numerical fire simulation to analyze smoke movement in the different fire source positions (center, edge, and corner) and various purlin heights (low: 10-20 cm; middle: 30-40 cm; high: 50-60 cm). The research further explored the influence of smoke detector response performance on each fire position and purlin height by setting up a 9 × 11 smoke detector array and several slices of smoke mass fraction. In this model, as the purlin height increased, the detectors arranged under them maintained their x and y coordinates, while the z coordinate moved down the corresponding height. The position of the detectors on the ceiling remained unchanged.

The findings showed the following: 1) The variation law of the smoke propagation path: In the scenarios of central and edge fires, smoke primarily spread along the horizontal direction of the main ridge. In those of corner fire, when the purlin height was low, the smoke tended to expand upward along the sloping roof; when the purlin height was high, the smoke propagated along the purlin in a "stepped" path. 2) The response time sequence of smoke detectors: For the center and edge fires, the differences in response time of smoke detectors affected by purlin height was approximately 30 s. For the corner fire, when the purlin height was below 20 cm, the detector at the main ridge responded within 60 s, and the variation in the response time of smoke detectors affected by the purlin height can reach up to 45 s. 3) The locations suggested for detector installation: When the purlin height is below 30 cm, the smoke detector should be installed at the center of the main ridge; when the purlin height is above 30 cm, the detectors should be placed on the ceiling near the center of the main ridge on both sides.

These findings provide technical support for the rational placement of fire detectors in similar "purlin style" buildings from the Ming and Qing dynasties to achieve comprehensive and timely early detection and warning of fire prevention in ancient buildings.

This study aimed to enhance the efficiency of indoor evacuation procedures by developing an optimal path-planning model based on the Dijkstra algorithm. The primary goal was to provide a dynamic and intelligent solution for guiding individuals to exit safely and swiftly during emergencies. Considering the complexities of indoor environments and the unpredictability of crowd behavior, the study highlighted the urgent need for a path-planning decision model that can adapt to real-time changes in crowd density and pedestrian flow velocity.

This study employed the single shot detector (SSD) algorithm for person detection and the deep learning-based simple online and real-time tracking (Deep-SORT) algorithm for multi-pedestrian tracking. These approaches, integrated with monocular vision techniques, enabled the extraction of pedestrian counts and flow velocities from architectural surveillance video streams. The Industry Foundation Classes standard was utilized to extract detailed architectural spatial information required for constructing a path network tailored for evacuation purposes. A novel method for predictive edge setting within the existing camera surveillance network was then introduced, allowing for the calculation of residual predictive edge pedestrian flow velocities. This calculation informed the development of an edge weight calculation method for the navigation road network, which is crucial for optimizing evacuation routes. These calculated weights were input into the Dijkstra algorithm to identify the shortest-time evacuation routes from any given node to the nearest exit.

The proposed model was validated through experiments conducted using a proprietary pedestrian database under simulated emergency conditions. The SSD algorithm achieved an average precision of 78.2% for pedestrian detection and counting, while the Deep-SORT algorithm achieved multiple object tracking precision and multiple object tracking accuracy scores of 71.2% and 78.5%, respectively. These metrics demonstrate the model's high accuracy in detecting and tracking pedestrians, a crucial aspect of effective evacuation planning. In addition, the model provided optimal path directions at each decision node, allowing for real-time adjustments as conditions change. Moreover, the system could adapt to fluctuating crowd dynamics, a critical feature given the unpredictable nature of human movement during emergencies.

This study demonstrated the feasibility of implementing a Dijkstra algorithm-based optimal path-planning model for indoor evacuations, achieving efficient and intelligent route optimization. The research provides a solid theoretical foundation and practical technical support for managing complex indoor evacuation scenarios, thereby contributing to the fields of emergency management and crowd control strategies. Further refinement and application of this model in real-world settings are expected to substantially enhance public safety measures. Additionally, the model's potential integration into existing building management systems presents a promising avenue for improving safety protocols and fostering a more systematic approach to handling indoor evacuations.