In order to enhance emergency response capabilities and the service efficiency of shelter systems in rural areas, this study adopted an optimal supply-demand allocation model integrated with Geographic Information System (GIS) software (ArcGIS) to evaluate the accessibility of village-level emergency shelters and township-level short-term shelters in northern Henan province. Key factors influencing shelter site selection were identified to construct a spatial suitability evaluation index system. Using this system, we assessed the spatial distribution of emergency shelters in Xicun town and proposed optimization strategies for the shelter system. The results reveal a significant supply-demand imbalance characterized by poor accessibility, partial overload of village-level shelters, and an irrational distribution with severe shortages of township-level shelters. The suitability evaluation indicates a clear north-south disparity, with higher suitability in the south. After implementing optimization measures—including adding village-level shelters and upgrading those in highly suitable areas—both the balance between supply and demand and overall accessibility are improved across the two-tier shelter system in Xicun town.

In response to new challenges, the application characteristics of dynamic network algorithms were reviewed. These challenges were posed by complex and diversified urban emergency scenarios. Heterogeneous data fusion and algorithmic collaboration were involved. Additionally, approaches for multi-algorithm collaboration and integration were explored. Firstly, based on 128 publications from the past five years in the Scopus database, four major research themes: path planning, traffic regulation, risk prevention, and resilience analysis, which were identified using keyword frequency statistics and cluster analysis. Then, through content analysis, four core algorithm categories: planning, simulation, clustering, and deep learning were summarized. Their theoretical frameworks and task adaptation logic were examined in conjunction with empirical data, simulations, and hybrid data sources. Finally, a multidimensional evaluation system was constructed to comparatively assess the applicability, strengths, and limitations of different algorithms across various emergency scenarios. The results show that multiple algorithms are integrated in application, which include planning, simulation, clustering, and deep learning. This integrated approach enables a multidimensional response to urban emergency management tasks. Such tasks involve path planning, traffic regulation, risk prevention, and resilience analysis. The adaptability of emergency systems are enhanced. The robustness of these systems is also improved. Data-driven intelligent algorithms further improve the responsiveness of urban road networks, supporting real-time strategy adjustment and resource optimization. Future development should focus on the construction of intelligent collaborative architectures for algorithm integration and coupled analysis across multiple networks, to advance the efficient application of dynamic network algorithms in multi-scenario emergency management.

In order to enhance the automatic recognition of safety hazards and improve safety management in construction scenarios, a multimodal large-model-based method for construction safety hazard recognition was proposed and its core component—the multimodal safety hazard recognition model, LLaVA(Large Language and Vision Assistant)-CS(Construction Site), was implemented. The system integrated images (construction site photos) with safety operating procedures (worker behavior descriptions), leveraging multimodal learning and deep learning technologies to perform real-time monitoring and analysis of construction sites. To support the system's effective operation, a multimodal dataset covering complex conditions such as varying lighting, occlusions, and multi-person scenarios was constructed, addressing the gaps in existing public datasets. Through prompt tuning of the LLaVA-1.5 model, the LLaVA-CS model effectively integrated visual and textual information, enhancing the accuracy and interpretability of safety hazard recognition. Experimental results show that this method achieves an accuracy of 0.722 2 in multiple real-world construction projects, generating detailed explanatory texts in real time to help managers quickly understand specific safety hazard contexts, thereby improving decision-making in safety management. This study innovatively applies multimodal large models to construction safety management systems, providing real-time, interpretable safety monitoring solutions and offering new technical support and optimization directions for construction safety management.

This study aimed to uncover the underlying mechanisms of emergency information processing during accidents and to improve the emergency response capabilities of individuals, thereby reducing injury severity. Based on theories of implicit memory and automatic processing, ERPs technology was employed in behavioral and neural experiments. The "reaction time" and "accuracy" were used as measures of information processing capacity. The ERP components P200 (P2), P300 (P3), and Late Positive Potential (LPP) were used to reflect the participants' cognitive and emotional processing. This study explored the automatic processing mechanisms of emergency information and identified its influencing factors. The results show that in frequent accident scenarios, experience-guided implicit memory enhances early attention sensitivity. This reduces conscious brain processing, automates information processing, and demands fewer cognitive resources. Conversely, infrequent accident scenarios trigger panic emotions, increase cognitive resource consumption, lead to cognitive fixation and attachment, and inhibit the automation of information processing.

In order to investigate the decision-making mechanism for operational safety risks in bridges and mitigate major operational safety hazards, a three-stage theoretical framework comprising risk perception, risk cognition, and risk decision-making for bridge operational safety was established. H-OWA operator was employed to process expert evaluations, constructing an interaction matrix for decision factors. The meanings and structure of concept nodes within FCM were defined to elucidate the mapping relationships between factors. Monitoring data collected during train passages on a specific railway bridge on January 1, 2020, was utilized for conducting engineering application and validation analysis. The results show that the weight importance ranking of bridge structure acceleration, strain, and displacement is the highest among the iterative process importance ranking, indicating their critical roles in perceiving bridge structure health. Moreover, the implementation effect of reinforcement strategies for the substructure of the bridge is significantly higher than that of waterproofing and insulation and reinforcement of the superstructure under low, medium, and high-risk scenarios, effectively enhancing the overall stability and safety of the bridge.

In order to improve the level of safety management in smart construction sites, it was necessary to establish a scientific and applicable evaluation index system and an evaluation model. Firstly, based on the trio spaces theory, the literature analysis method, the Delphi method and the field survey method were employed to construct a maturity evaluation index system of safety management in smart construction sites, including 5 first-level indicators and 24 second-level indicators. Secondly, the weights of these evaluation indicators were determined by the game theory-combination weighting method. A maturity evaluation model was then constructed based on the extension cloud theory, putting forward five maturity levels. Finally, the maturity evaluation model of safety management was applied to a smart construction site in Suzhou. The results show that the maturity score of safety management in this smart construction site is 78.304, with a standard level, which is consistent with the actual safety management condition on-site, and the validity and scientificity of this model are verified. Through the priority area improvement method, the repeated incidence of unsafe behavior of personnel, accuracy rate of dangerous behavior identification and early warning, closed-loop security inspection rate, allocation rate of intelligent security protection equipment and accuracy rate of equipment unsafe state monitoring and early warning should be improved first.

In order to control the dust pollution problem of coal mine transportation roadway, the geometric model of Qipanjing coal mine transportation north roadway was established according to the ratio of 1∶1 by using numerical simulation software. Firstly, based on the standard k-ε turbulence model and particle tracking model, the airflow-dust coupling characteristics of the transportation roadway were studied, and a multi-section spray dust reduction technology for simultaneously controlling the transfer point and the transportation roadway was proposed according to its characteristics. Then, the effectiveness of the new dust reduction technology was verified by the combination of numerical simulation and experiment. Finally, the field application was carried out in the north roadway of the transport roadway in Qipanjing Coal Mine to verify the practicability and high dust reduction characteristics of the technology. The results show that the airflow distribution in the transportation roadway is affected by the running speed of the belt and the traction force to produce the induced airflow. The overall average airflow velocity is about 0.6 m/s, and the dust particles are dispersed by the airflow of the roadway. The dust with a particle size of less than 25 μm is suspended at the sidewalk, while the dust with a particle size of more than 25 μm is deposited on the surface of the belt and the floor of the roadway. The new dust reduction technology realizes the perfect coverage of the transfer point, and a supersonic fog curtain is formed in the main roadway to intercept and capture the dust that escaped from the transfer point. The test results show that the supersonic spray has a large range of fog curtain, strong wind resistance and significant continuous dust reduction effect. Through the field application of the north roadway of the transport roadway in Qipanjing Coal Mine, the highest dust reduction efficiency of total dust and respirable dust reached 80.12% and 83.15% respectively.

To improve the accuracy of structural safety evaluation results for hazardous chemical storage tanks, a finite element model construction process for storage tanks based on 3D laser scanning was proposed. This process utilized point cloud data to build a finite element simulation model that accurately reflected the actual structural topology of the tank, such as out-of-roundness and local concave-convex deformations, thereby enhancing the accuracy of the analytical model. First, high-precision spatial structural data from the field were acquired using a 3D laser scanning system. A lightweight regularization preprocessing method was proposed, including coordinate system transformation, node correspondence, and coordinate value updates. Next, combined with the finite element model unit construction method, the point cloud nodes with updated coordinates were used to generate a numerical simulation model that accounted for initial geometric defects, followed by an analysis of model accuracy. Finally, taking an in-service dome-roof tank as an example, deformation analysis of the storage tank was conducted based on 3D laser scanning data. A high-precision finite element model construction method was employed to build a full-scale finite element model of the tank wall, and an evaluation of the wall's strength performance was carried out. The results indicate that the point cloud data processing method proposed in this paper enables the construction of a finite element model that accounts for geometric deformation of storage tanks. The spatial position accuracy of the unit nodes can reach 0.01 mm, and the volume of the reduced point cloud data can be decreased by 70%. Compared to traditional finite element simulation models that consider initial geometric deformation, the structural safety evaluation data obtained from this model are more conservative, leading to safer assessment conclusions for storage tanks.

This study provides quantitative support for analyzing current fire communication command systems and enabling their iterative upgrades. A four-level efficacy evaluation index system for brigade-level fire command communication systems was constructed, based on fire communication command system design specifications. This system assessed three key dimensions: operational support capability, data service capability, and communication assurance capability. An IPSO-BP-based system efficacy evaluation method was proposed, building upon BP neural network algorithm. Parameters were optimized using IPSO algorithm. Sample data were acquired through a combination of expert scoring and the Analytic Hierarchy Process (AHP). Principal Component Analysis (PCA) was applied for dimensionality reduction. Simulation comparisons were conducted using three distinct models: BP neural network, PSO-BP neural network, and IPSO-BP neural network. Results demonstrate that IPSO-BP neural network model achieves the fastest convergence speed. Its mean square error decreases by 75.71% compared to BP neural network model and by 45.96% compared to PSO-BP neural network model, representing the lowest error value among the three models. Furthermore, IPSO-BP model reasonably and accurately evaluates brigade-level fire communication command system efficacy, demonstrating considerable generalizability.

In order to reduce couriers' unsafe behaviors caused by job burnout and effectively avoid safety accidents, this study aimed to explore the influencing factors and management countermeasures of couriers' unsafe behaviors based on the Stimulus-Organism-Response (SOR) theoretical framework, employing mixed research methods. Firstly, it integrated data from on-site interviews and questionnaires, and analyzed the action paths of work burnout and safety attitude on couriers' unsafe behaviors from the four-dimensional factors of man, machine, environment, and management. Secondly, it constructed a conceptual model that includes the four-dimensional factors (man, machine, environment, and management), work burnout, safety attitude, and unsafe behaviors. Finally, it used Smart PLS 4.0 software to verify the structural equation model and put forward corresponding management improvement strategies for the state, express enterprises, and couriers based on the analysis results. The results show that work burnout and safety attitude have a significant positive impact on couriers' unsafe behaviors. Among them, the four-dimensional factors of man, machine, environment, and management indirectly affect unsafe behaviors through the mediating role of work burnout; the three-dimensional factors of machine, environment, and management indirectly affect unsafe behaviors through the mediating role of safety attitude (p<0.05).