China Safety Science Journal

Exploration and practice of a new training mode for safety engineering degree postgraduates

HU Juan, WANG Kai, TONG Ruipeng, ZHOU Aitao

2025, 35(1): 1-6. doi:10.16265/j.cnki.issn1003-3033.2025.01.1037

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Cultivating professional master's students is essential to addressing the shortage of high-level applied talents. To meet the industry demand for safety engineering professionals, this study analyzes challenges in China's training processes based on domestic and international practices. It introduces the "professional group + action learning method" model, alongside reforms in curriculum, teaching methods, training bases, faculty, and evaluation standards, using China University of Mining and Technology-Beijing as a case study. Data from the 2023 cohort validate the model's effectiveness in improving graduate quality, enhancing competencies, and addressing traditional education shortcomings, proving its feasibility and reference value.

Comparative study of inherent safety, behavior-based safety, process safety and functional safety based on safety management paradigm shift

TONG Ruipeng, WANG Lulu, XU Surui, WANG Zhihao, LIAN Fangfei

2025, 35(1): 7-15. doi:10.16265/j.cnki.issn1003-3033.2025.01.1045

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To clarify the essential characteristics and differences between inherent safety, behavior-based safety, process safety, and functional safety and to promote a virtuous cycle of high-quality development and high-level safety, this study employed literature review and comparative analysis methods to explore their basic connotations and evolution processes, interrelationships, realistic challenges, and development paths based on the safety management paradigm shift. The results indicate that inherent safety is an idealized form of safety. Behavior-based safety is an interdisciplinary field that involves the theories and methods of safety science and behavioral science. Process safety protects humans, machines, and the environment through systematic approaches from a full life cycle perspective. Functional safety aims at preventing unacceptable risks caused by functional failures of systems. The four types of safety, led by inherent safety, involve a gradual progression from concepts to practice. These types of safety share a unified internal structure encompassing the elements of humans, machines, environment and management. The current representative standards cover various industry sectors and focus on accident prevention. In the future, the synergistic effect of the four in safety governance should be fully utilized. By using artificial intelligence technology to empower the new engine of safety production, the four should be continuously improved in specific practices tailored to local conditions.

Risk assessment of biological sample transport by UAVs based on Bayesian networks

LIU Qing, SHEN Tian

2025, 35(1): 16-24. doi:10.16265/j.cnki.issn1003-3033.2025.01.0441

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To quantify the transportation risks associated with biological samples using UAVs, this study first identified 32 risk factors across five dimensions-human, machine, environment, management, and hazard-based on national standards and relevant literature. A BN for risk assessment was constructed using Netica software, with prior probabilities determined through expert knowledge and fuzzy set quantitative analysis. The proposed risk assessment model was then used for bidirectional reasoning and scenario analysis. A case study of a UAV company in Shenzhen was presented to evaluate the transportation risks of biological samples and identify key influencing factors. The results indicate that the risk probability of biological sample transportation, as calculated through forward reasoning, is approximately 2.203×10^-5. The primary risk factors are related to hazardous materials, followed by equipment and facility-related issues. The core risk factors influencing biological sample transportation include the size, quantity and weight of hazardous material packages, the temperature control effectiveness of specialized cold chain logistics boxes, the integrity of emergency response plans, emergency handling capabilities, safety management and education, and the presence of obstacles.

Thermal safety risk warning system for elderly people based on thermoregulation model

ZHENG Guozhong, JIA Ruilin, YI Wenwen, YUE Xinru

2025, 35(1): 25-31. doi:10.16265/j.cnki.issn1003-3033.2025.01.0759

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A thermal safety warning system was established based on a thermoregulation model to mitigate the thermal safety risk for elderly people under high-temperature conditions. Firstly, the improved model suitable for the elderly was established by replacing the physical model and adjusting the active and passive systems in the classical model. The improved model was validated using publicly available experimental data. Secondly, the model simulated the temperature changes in elderly people in high-temperature environments. Statistical analysis was used to assess the impact of various parameters on thermal safety. Finally, based on the analysis results and the improved model, a thermal safety risk warning system for elderly people was developed. The warning system was demonstrated through a case study. Results indicate that the improved model accurately simulates the body temperature of elderly people, with a root mean squared error less than 0.12 ℃. Physical activity intensity significantly impacts thermal safety, with a standardized regression coefficient β larger than 0.8. As heat exposure time increases, the impact of activity intensity on thermal safety is decreased (β decreases from 0.945 to 0.806), while the influence of environmental factors is increased (β of temperature and humidity increases from 0.249 and 0.137 to 0.370 and 0.348). In the case study, the safe duration of continuous activities for the resting and working elderly people in Baoding/Hong Kong is 172/175 minutes and 108/122 minutes, respectively. The highest thermal safety risk period for elderly people on that day is between 17:00 and 18:00.

Quantitative classification and evolution analysis of construction safety climate

LEI Tian, TIAN Zhen

2025, 35(1): 32-39. doi:10.16265/j.cnki.issn1003-3033.2025.01.0396

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In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

Visual behavior for construction site hazard identification under different cognitive loads

DENG Hongze, KONG Yuanyuan, XU Sheng

2025, 35(1): 40-49. doi:10.16265/j.cnki.issn1003-3033.2025.01.0685

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In order to explore the effect of cognitive load on workers' hazard identification behavior, a cognitive experiment based on eye-tracking technology for construction site hazard identification was designed firstly. In this experiment, a N-digit task was introduced to increase the cognitive load. Secondly, the gaze and glance data were collected to analyze the static attention allocation, and the scanning path was processed to extract the dynamic transfer characteristics of attention. Finally, three parameters of variance review probability (RP), transition probability (TP) and switching probability (SP) were selected as the quantitative parameter values to classify the scanning patterns of hazard identification, which explored the influence of cognitive load on hidden hazard identification from the perspective of visual behavior performance. The results show that the level of cognitive load negatively affects hazard identification performance. The subjects with high cognitive load show longer first fixation time, fewer fixation counts and saccade counts, and there is no significant difference in fixation percentage and mean fixation duration. Additionally, based on the attention characteristics, three scanning patterns are identified: sequential inspection, repeated comparison and random discovery. With the improvement of cognitive load level, subjects will pay more attention to identifying single hazard but neglect others during sequential inspection, and reduce the attention in the hazard area but still keep the fixation point quickly and frequently switching during repeated comparison, while the number and time of inspection of hazard areas are reducing simultaneously during random discovery.

Mechanical response of high pre-tightening stress uniformly distributed bolt under static-dynamic load

HOU Junling, LI Chuiyu, HUANG Tongli, YUAN Lin, LIU Shiyang, HUANG Ping

2025, 35(1): 50-59. doi:10.16265/j.cnki.issn1003-3033.2025.01.1389

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In order to solve the problem of bolt breaking and losing anchor due to the influence of strong mining stress in deep mining roadway, and effectively improve the pre-tightening force of the bolt, a stress uniform bolt with high pre-tightening force was designed. The theoretical analysis, static load drawing and drop hammer impact test, numerical calculation and DIC method were used to study the load-displacement distribution characteristics of the bolt in the process of static load drawing, and the stress concentration position of the bolt was obtained. The mechanical response characteristics of the bolt under dynamic load were studied by using Ansys numerical simulation of drop hammer impact test and SHPB impact test. The results show that the thread deformation of the ordinary bolt nut decreases exponentially along the axial direction away from the extrusion surface, and the strain is concentrated in the first three circles of the thread. The stress uniformly distributed bolt can achieve thread strain coordination, and the mechanical environment is good. The change of bolt axial force is divided into a rising zone, an oscillating zone and a stable zone. When the drop hammer impulse is the same, the stress uniform distribution bolt can reduce the impact force to 64% of the original, and reduce the amplitude frequency of bolt axial force. The stress uniform distribution bolt can reduce the amplitude and frequency of the stress wave waveform. The research results have been applied to deep roadways such as Zhuji mine, Paner mine and Dingji mine. The bolt has no broken anchor phenomenon, and the control effect of the roadway surrounding rock is good.

Mining truck driver fatigue driving detection based on improved YOLOv8

GU Qinghua, YIN Shutan, WANG Dan, LI Xuexian, YIN Huimin

2025, 35(1): 60-66. doi:10.16265/j.cnki.issn1003-3033.2025.01.0147

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To address the high rates of missed detections and false alarms, as well as the poor robustness in fatigue driving detection for open-pit mine truck drivers, a fatigue driving detection model for mine truck drivers (EBS-YOLO) based on the improved YOLOv8 is constructed to enhance the overall performance of fatigue detection. Firstly, YOLOv8 was used as the basic model for fatigue detection, and a small target detection layer was added to enhance the model's attention to small targets. Secondly, the bottleneck attention module (BAM) was used to improve the model performance to extract small target features, especially eye features. Finally, all cross-stage aggregation modules (C2f) in the backbone network were replaced with efficient multi-scale attention (EMA) modules, thereby effectively reducing model parameters and computational overhead to meet the requirements of a lightweight model. The results showed that the improved YOLOv8 model had a great detection effect with the accuracy, recall rate, and average detection accuracy reaching 93.6%, 93.9%, and 96.5%, respectively, and the memory size of the model was only 4.9 MB. Compared with the YOLOv8 model, the improved model can quickly and accurately identify the fatigue state of mining truck drivers, meet real-time requirements, and effectively prevent fatigue-driving accidents.

Model of pavement pothole target detection with improved YOLOv5s

ZHAO Jiangping, WANG Xinran, WU Lizhou

2025, 35(1): 67-74. doi:10.16265/j.cnki.issn1003-3033.2025.01.0619

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To improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work, reduce the probability of traffic accidents. A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed. This method incorporated the ASFF module into the original YOLOv5s network, replaced the backbone network with the FasterNet network, and further introduced the Efficient Channel Attention (ECA) module. Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model, to verify the target detection effect, and to develop an interactive visualized detection interface. The results show that the improved model accuracy, recall rate, and average detection accuracy have increased by 4.1%, 9.9% and 5.6% respectively. Compared to the original network, the improvement is significant. It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves, thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.

A lightweight forest fire detection algorithm based on YOLOv5s

LIU Huilin, FANG Qiong, JIANG Yu, WEI Huazhang, WANG Tao, ZHANG Shuchuan

2025, 35(1): 75-83. doi:10.16265/j.cnki.issn1003-3033.2025.01.0127

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In order to solve the problems of complex structure, large scale and difficulty in balancing detection accuracy and efficiency of the current forest fire detection algorithm based on deep learning, a lightweight forest fire detection algorithm based on YOLOv5s was proposed. Firstly, an optimized background difference technique was used to eliminate the interference of fire-like objects in the background image, thus reducing the time required for image analysis. Secondly, a group blending strategy was designed to optimize the conventional convolution, and an efficient channel attention (ECA) mechanism and depthwise separable convolution were incorporated into the C3 module of feature extraction, which enhanced the ability of image feature extraction and fusion and at the same time effectively reduces the number of model parameters. Then, a dynamic non-monotonic focusing mechanism was used to optimize the WIOU loss function, reducing the harmful gradients generated by low-quality samples. Finally, sufficient experimental comparisons between the proposed algorithm and other algorithms on the constructed forest fire dataset. The results show that the proposed algorithm shows good generalization in various scenarios, and the detection accuracy of the flame target can reach 86.1%, which is 2.7% higher than that of the standard YOLOv5s, and the detection speed is increased by 11.4%, which effectively reduces the fire false alarm rate and enhances the detection performance of the model.

Cognitive test of construction safety education effect based on VR technology

DING Chao, ZHANG Xiangke, WANG Kun, SONG Ziwei, GUO Xiaowen

2025, 35(1): 84-93. doi:10.16265/j.cnki.issn1003-3033.2025.01.0181

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In order to make up for the deficiency of VR technology in safety education theory support and practical effect tests, the relevant influencing factors affecting the effect of engineering safety education were taken as the entry point. SEM was used to establish an analytical framework for the influencing factors of safety education and the cognitive experiments were carried out to test the effect. The results show that the frequency of education, the mode of education, individual initiative, fun and comfort are the main factors affecting the effectiveness of construction safety education. The hybrid mode of "VR + lecture" is the most effective educational method at present, but the use of VR technology still requires personnel assistance. The optimal frequency of education is recommended to be less than 30 days. The main factors of fun, comfort are significantly positively correlated with educational effect, but comfort is more important in the application of construction safety education with VR technology.

Effects of excavation support of deep and large foundation pit on gravel stratum on adjacent existing road

GUO Yanhui, MA Rui, MAO Shilin, QIAN Yong, DING Mingzhong, SONG Qin

2025, 35(1): 94-102. doi:10.16265/j.cnki.issn1003-3033.2025.01.0025

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To prevent accidents and disasters, such as the collapse of existing roads nearby, caused by deep and large foundation pit excavation and support, it is necessary to ensure the safe operation of adjacent roads during the construction of deep and large foundation pits. Taking a deep and large foundation pit project near a city trunk road in Kunming as an example, on the basis of in-depth research at the site, the three-dimensional finite element numerical simulation software-new eXperience of GeoTechnical analysis system(MIDAS GTS NX) is used for simulation and calculation, and combined with on-site monitoring, to analyse the force and deformation characteristics of the foundation pit support structure and the deformation characteristics of the adjacent existing road under the existing excavation and support scheme. The study results indicated that the forces and deformations of the supporting piles and anchor cables were within design limits after the foundation pit excavation and support were complete. The displacement near the existing road increased with the increase of pit excavation depth, and become stable after the excavation was completed. The maximum deformation of the existing road occurred at a position 2.5 times the excavation depth from the foundation pit boundary. Furthermore, the deformation did not reach the alarm threshold for road displacement caused by foundation pit excavation. Thus, the existing support scheme can ensure the safety of both the foundation pit and the adjacent road.

High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model

JIANG Yuanliang, REN Qingying, REN Yuan, LIU Haipeng, DONG Shaohua

2025, 35(1): 103-111. doi:10.16265/j.cnki.issn1003-3033.2025.01.0632

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An improved object detection algorithm for high-consequence areas was proposed to solve the problems of the sensitive and complex external environment of the overseas section of the China-Myanmar oil and gas pipeline, difficulty in manual inspection, and high-risk factors. Firstly, a convolutional block attention module was used to adaptively learn channel and spatial attention to enhance the network's perception and generalization capabilities. Then, focal and efficient intersection over union(Focal-EIoU) loss was used to comprehensively consider the target features and their associations to deal with the issues of class imbalance, reduce the interference of easy-to-classify samples, and enhance the robustness of the model. Finally, the improved model was used to intelligently recognize regional attributes of China-Myanmar oil and gas pipeline remote sensing images. Furthermore, the proposed YOLO model was validated against related ablation experiments. The results showed that for the feature recognition of remote sensing images of the China-Myanmar oil and gas pipeline, the proposed model reached a mean average precision (mAP) of 68.2% for the field, green space, settlement, and river. The model performance was improved by 29%, 21.6%, and 10.7% compared with YOLOv5, YOLOx, and YOLOv8, respectively.

Research on vehicle hazardous cut-in strategy used in autonomous driving test

ZHOU Yang, CHEN Yunxing, WU Ling

2025, 35(1): 112-119. doi:10.16265/j.cnki.issn1003-3033.2025.01.0540

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To improve the interaction ability of traffic vehicles in the cut-in scenario, a method for constructing a vehicle hazardous cut-in strategy based on deep reinforcement learning was proposed. Firstly, a simulated environment was built based on scalable multi-agent reinforcement learning training school(SMARTS) simulation platform. Then, twin delayed deep deterministic policy gradients (TD3) algorithm was adopted to train an agent to cut in a randomly chosen target vehicle hazardously. The algorithm was compared with proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms. The trained model was tested in seven different scenarios with varying traffic densities. Finally, a multi-agent testing environment was built, and the trained model was applied to validate intelligent driving strategies. The results show that the success rate of hazardous cut-ins reaches 80.35% in model training with TD3 algorithm, outperforming both comparative methods. In model testing, except for the 2 700 vehicle/h test scenario, the model achieves a hazardous cut-in success rate of over 80% in the other three test scenarios that were not used in training, demonstrating good generalization ability. Meanwhile, the time to collision values between the ego vehicle and the target vehicle at the moment of lane changes are concentrated within the range of 0 to 6 seconds, with 95% falling within this bracket. The proportions of time to collision values in the intervals of (0,2], (2,4], (4,6]s are 60%, 30%, and 5% respectively, covering test conditions with different collision risk. In the validation of intelligent driving strategies, the traffic vehicle controlled by the trained model can actively perform cut-ins in front of the test vehicles, exposing it to the risk of a rear-end collision and helping in identifying safety vulnerabilities in intelligent driving strategies.

Research on fatigue crack propagation of injection-production string in gas storage facilities under alternating pressure load

ZHANG Yu, ZHANG Gang, ZHANG Qiang, MA Qun, QIN Xiaojie, JIA Rui

2025, 35(1): 120-126. doi:10.16265/j.cnki.issn1003-3033.2025.01.0543

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To improve the safety risk prediction accuracy of injection-production string, a joint simulation analysis method was used to perform finite element verification of the fatigue crack propagation process of straight-notch compact tensile specimens. Then, a finite element model of fatigue crack propagation of injection-production string with external surface cracks in gas storage was proposed. Moreover, the injection-production strings' fatigue crack propagation behavior was analyzed under alternating pressure loads. The results indicated that the specimen simulations were consistent with the fatigue crack test results, indicating that the joint simulation method had high accuracy in fatigue crack propagation analysis. During the fatigue crack propagation of the injection-production string, the larger the initial crack's length-to-depth ratio or the higher the stress ratio, the faster the crack propagation rate with a critical minimum crack length of 4 mm. Under the same number of pressure load cycles, the larger the initial crack's circumferential angle, the longer the surface crack propagates, and the critical minimum circumferential angle was 45°. The crack consistently propagated along the axis of the pipe string regardless of the initial crack's circumferential angle, and the unstable propagation length was 52 mm. During alternating injection and production in gas storage, the amplitude of the alternating pressure load should be properly controlled to avoid the initiation and propagation of fatigue cracks.

Study on catalytic oxidation characteristics and influencing factors of Cu-Mn type CO catalyst

ZHOU Luhan, JIANG Yanhang, LI Xu, SUN Yashengnan, WANG Lixin

2025, 35(1): 127-136. doi:10.16265/j.cnki.issn1003-3033.2025.01.0309

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In order to study the effect of different metal doping and precipitation temperatures on the catalytic oxidation of CO by Cu-Mn type catalyst, the co-precipitation method was used to prepare Cu-Mn type CO catalyst, and the catalytic oxidation of CO by Cu-Mn type CO catalyst under different metal doping and precipitation temperatures was tested and analyzed. The pore characteristics and surface crystal structure of the catalyst were obtained by automatic physical adsorption analyzer and X-ray diffraction (XRD). The reaction process of catalytic oxidation of CO was revealed by in-situ diffuse reflection infrared spectroscopy, and the potential application of the catalyst in coal mines was introduced. The results show that during the test time (within 80 s), with the increase of reaction time, the volume fraction of CO gradually decreased, slowly increased and then tended to be flat, and the amount of reactive CO substance gradually increased. The better the catalytic oxidation of CO, the larger the specific surface area, the smaller the average pore size and the larger the total pore volume. When the doped metals are Sn, Fe and Ce, the catalytic oxidation characteristics of the three catalysts are as follows: CuMnO_x-Ce>CuMnO_x-Sn>CuMnO_x-Fe, the amount of CO involved in the reaction was 0.015 3, 0.009 3 and 0.020 3 mol, and the removal efficiency of CO was 61%, 47% and 77%, respectively. When the precipitation temperature is 70 ℃, the number of crystal nuclei of the catalyst is significantly higher than that of the precipitation temperature is 60 and 80 ℃. When the precipitation temperature is 60, 70 and 80 ℃ respectively, the catalytic oxidation characteristics of the three catalysts are as follows: CuMnO_x-Ce-70>CuMnO_x-Ce-80>CuMnO_x-Ce-60, the amount of CO involved in the reaction was 0.019 45, 0.020 3 and 0.019 8 mol, and the elimination rates of CO were 74%, 77% and 75%, respectively. Abundant surface oxygen vacancy is the key factor to improve the performance of CO oxidation reaction and catalytic oxidation. The presence of CeO₂ contributes to the formation, oxygen activation and migration of carbon-containing species.

Study on competitive adsorption of O₂/CH₄ mixed gas by coal from molecular perspective

HE Shujing, PENG Chengyang, KANG Jianhong, ZHANG Gengxian

2025, 35(1): 137-145. doi:10.16265/j.cnki.issn1003-3033.2025.01.0485

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To deeply understand the competitive adsorption characteristics of O₂/CH₄ gas mixtures on the coal surface, the pore distribution and chemical structure of the coal surface were analyzed using the Grand Canonical Monte Carlo(GCMC) method. Molecular simulation was used to investigate adsorption capacity and heat variations during the competitive adsorption of O₂/CH₄ gas mixtures under varying concentrations, pressures, and temperatures. Moreover, the competitive adsorption mechanism of O₂/CH₄ gas mixtures on the coal surface was revealed by adsorption selectivity. The results indicated that the isothermal adsorption curves were identical when the concentration ratio of O₂ to CH₄ was 7/3. The integrated adsorption heat under different conditions was approximately 19.5-20.0 J/g for O₂ and 24.0-24.8 J/g for CH₄. When the concentration ratio of O₂ to CH₄ ranged between 7/3 and 2/8, the integrated adsorption heat of O₂ was higher than that of CH₄. The increased temperature decreased the adsorption capacity of both gases but increased the adsorption selectivity of O₂ to CH₄. Moreover, the effect of gas pressure on competitive adsorption weakened with pressure increase. CH₄ adsorbed faster than O₂under low gas pressures, and CH₄ reached saturation adsorption earlier than O₂when the total pressure increased. When the O₂ concentration in the gas mixtures increased, the adsorption selectivity of O₂ for CH₄ decreased. However, CH₄ consistently showed higher adsorption competitiveness than O₂.

Research on pedestrian flow characteristic model and instability based on controlled experiment

ZHENG Tao, ZHOU Jibiao, MAO Xinhua, DONG Sheng, ZHANG Zhenya

2025, 35(1): 146-153. doi:10.16265/j.cnki.issn1003-3033.2025.01.0895

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In order to investigate the threshold conditions for pedestrian destabilization, controlled experiments were conducted in three different scenarios, including "two-person collision experiment", "multiple-person collision experiment" and "high-density multiple-person collision experiment". A pedestrian flow model was constructed, considering collision pressure, speed, density and volume. Additionally, experimental videos and collision pressure data were collected by using an unmanned aerial vehicle(UAV)and pressure sensing equipment, respectively. The changing rules of pedestrian trajectory, pedestrian flow characteristics, and collision pressure were obtained by data analysis. On this basis, the segmented Hermite triple interpolation was used to investigate the influence of speed and density on collision pressure. Finally, based on the Van Aerde model, a four-dimensional model of "pressure-speed-density-volume" was constructed by introducing the parameters of collision pressure to judge the threshold conditions of pedestrian flow instability. The results show that pedestrian flow begins at 2.46 persons/m² and reaches complete instability at 3.59 persons/m². During the instability process, the pedestrian may be subjected to the collision pressure ranging from 187.32 N to 258.11 N. The results provide references for the control of the pedestrian flow and the improvement of pedestrian traffic safety.

Resilience assessment of fire safety in subway stations based on WSR-extension cloud theory

TANG Qinghui, LIU Shuo, ZHANG Jin

2025, 35(1): 154-162. doi:10.16265/j.cnki.issn1003-3033.2025.01.1377

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In order to solve the problems of ambiguity and randomness in the process of fire safety resilience assessment of subway stations, and then effectively improve the level of fire safety resilience of subway stations, the fire safety resilience assessment model for subway stations based on WSR- extension cloud theory was constructed. First, based on WSR methodology, the factors affecting the fire safety resilience of subway stations were analyzed around "physical", "matter", and "human". Combined with the characterization of the resilience absorption, resistance, recovery and adaptive ability, the fire safety resilience assessment index system of subway stations was established in five aspects, namely, equipment factors, environmental factors, organization and management, material and technology, and personnel factors. Second, based on the blind number theory to construct the blind number matrix and calculate the comprehensive score of qualitative indexes, the fire safety resilience level of subway stations is derived by using the theory of extension cloud theory. Finally, a station of the Qingdao subway was used as an example to carry out the example analysis. The results show that the subway station fire safety resilience level of Ⅳ, in the higher resilience level, the credibility factor = 0.003 4 <0.01, indicating that the assessment results have a high degree of credibility. The WSR-extension cloud theory assessment model can provide a theoretical basis for the fire safety resilience assessment of subway stations.

Influence of classroom structure layout on evacuation efficiency of young primary school students

WANG Jun, HU Yanghui, CHEN Xianfeng, SUN Xuxu

2025, 35(1): 163-170. doi:10.16265/j.cnki.issn1003-3033.2025.01.0526

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In order to improve the evacuation efficiency of the teaching building, the classroom structure was optimized through control experiments and numerical simulations to enhance evacuation efficiency. Emergency evacuation tests were used to obtain the movement characteristics of students aged 6-7 years old. And Pathfinder simulation software was used to study the impact of desk layout, classroom door position, and exit position on evacuation. The results indicate that for a single classroom, although shortening the pre-action time can reduce the overall evacuation time, it cannot improve the congestion caused by the building structure. Appropriate evacuation routes and desk layouts can significantly reduce evacuation time. For buildings with classrooms on one side of the corridor, increasing the width of the corridor and exit is the most effective way to improve evacuation efficiency. For buildings with classrooms on both sides of the corridor, the structure of the evacuation corridor inside the building, including the number of corridors and the intersections inside the corridors, is the most important factor affecting evacuation time. Therefore, it is recommended to develop optimization plans for classroom evacuation structure from different aspects.

Study on reflective strip configuration and driving simulation in highway tunnels based on vehicle distance maintenance

MEI Jialin, DU Zhigang, HE Shiming, WANG Shoushuo

2025, 35(1): 171-177. doi:10.16265/j.cnki.issn1003-3033.2025.01.0176

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To reduce the risk of rear-end collisions in highway tunnels, a tunnel reflective strip space model was proposed based on the tunnel reflective strip characteristics with depth perception information. The effects of spacing between tunnel reflective strips on vehicle distance maintenance were examined through driving simulation tests. Braking headway, minimum headway, following headway, and minimum collision time were selected as evaluation indicators. The results indicated that when the leading vehicle traveled at speeds between 40-80 km/h and the following vehicle approached at a speed 20 km/h higher than the leading vehicle, if the following driver received 3-4 visual stimulations from the reflective strips before the distance between the leading and following vehicles was less than the minimum safe distance, the braking headway, minimum headway, and the time headway under stable car-following conditions was improved by 27.6%-56.6%, 54.2%-60.3%, and 20.1%-31.6%, respectively. Furthermore, the minimum collision time was increased by 34.7%-60.5% once the leading vehicle braked urgently, reducing the probability of a rear-end collision. Therefore, tunnel reflective strips enhance drivers' perception of speed and distance perception ability, reducing the risk of rear-end collisions.

Assessment of regional landslide susceptibility based on fractal theory and information amount method of GIS

CAO Hongyang, REN Xiaoying, LI Zhiqiang

2025, 35(1): 178-185. doi:10.16265/j.cnki.issn1003-3033.2025.01.0480

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In order to reduce the losses caused by landslide disasters, taking Changde city of Hunan province as an example, the fractal theory and information method were applied to evaluate the regional landslide susceptibility based on field investigation and historical landslide data. The sensitivity of influence factors was quantitatively studied by fractal theory. The information values of each secondary impact factor were obtained by using the information method, and the comprehensive information values were obtained by combining the fractal dimension value and the information value. Based on the values, the susceptibility zoning of the study area was carried out. The results show that the slope, engineering geological rock group, elevation and vegetation are the influencing factors that have a second-order cumulative and fractal distribution with the landslide, while other influencing factors have a first-order cumulative and fractal distribution with the landslide. The areas of very low, low, medium, high and very high susceptibility areas respectively account for 5.24%, 8.84%, 35.06%, 39.21% and 11.65% respectively. Annual rainfall greater than 1 600 mm, slope of 20-30° and elevation of 900-1100m are important factors.

Emergency supply distribution model and its algorithm considering post-disaster fuzzy demand and road damage

ZENG Xiaoqing, LIU Liming, CHENG Zeyu

2025, 35(1): 186-193. doi:10.16265/j.cnki.issn1003-3033.2025.01.0374

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In the early stages of a major disaster, where demand at disaster sites is uncertain, roads are damaged in affected areas, and the fairness and timeliness of rescue operations must be considered, the SNS algorithm is applied to solve the emergency supply distribution model to achieve rapid and effective distribution of emergency supplies. First, an emergency supply distribution model was constructed, with the objective of minimizing the total cost of emergency rescue and the evaluation of humanitarian aid under the background of fuzzy demand and damaged roads. Then, the SNS algorithm was introduced to solve the model, and an improved SNS(ISNS) algorithm with a reinforcement learning rate strategy was proposed. Finally, taking the 2022 Luding Earthquake in Sichuan as an example, the SNS algorithm, ISNS algorithm, discrete particle swarm optimization, genetic algorithm, and simulated annealing algorithm were applied to solve this case, respectively. The results indicate that the ISNS algorithm demonstrates stability. Compared with other algorithms, the total cost of emergency rescue is reduced by at least 6 410 yuan, and the evaluation of the humanitarian aid evaluation target is improved by at least 50.6%, highlighting the superiority of the ISNS algorithm. The ISNS algorithm is beneficial for solving emergency supply distribution problems.

Emergency rescue equipment support capability assessment model

ZHENG Xuezhao, DONG Beibei, TONG Xin, WANG Yishuo

2025, 35(1): 194-201. doi:10.16265/j.cnki.issn1003-3033.2025.01.0742

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To ensure that the equipment support system of professional emergency rescue teams could meet the requirements of rescue tasks and gradually adapt to complex and variable disaster risks, a text mining method was applied to analyze the factors influencing the equipment support capacity of emergency rescue. Based on technical personnel support capability, equipment resource support capability, equipment and facility support capability, information resource support capability and management system support capability, an assessment index system for emergency rescue equipment support capacity was developed. To reduce the impact of fuzziness, randomness, and subjective-objective bias on assessment results, a combined weighting method was adopted to determine the weights of each assessment index. A comprehensive assessment method was established using the matter-element extension model and the integrated cloud model. Professional emergency rescue team A was selected as an example for application to verify the scientific validity and effectiveness of the model. The results show that the index system comprehensively and accurately reflects the overall level of emergency rescue equipment support capability of professional teams. The assessment model reasonably and effectively assesses the capability level and identifies weaknesses in the current equipment support system, providing improvement points and theoretical support for the development of the team's equipment support system.

Study of factors influencing rural emergency response capacity based on multi-criteria decision-making method

LI Hua, JING Zihao, WU Lizhou, GAO Zitong

2025, 35(1): 202-208. doi:10.16265/j.cnki.issn1003-3033.2025.01.0442

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To explore the "last-mile" problem of grassroots emergency response capacity, a MCDM based on grey theory was proposed to analyze the rural emergency response capacity under conventional and unconventional states based on the resilience theory. Firstly, from the perspective of resilience, the influencing factors obtained from literature review and field investigation were selected and optimized to construct a model of the influencing factors of rural emergency response capacity. Secondly, the MCDM model was used as a framework to analyze the causality, logical hierarchy, and characteristic state of the influencing factors. Finally, the key factors for the enhancement of the rural emergency response capacity and the resilience of rural villages were identified through the multi-criteria decision-making analysis. The results show that the centrality of leadership team structure is 2.95 and the driving force is 6, which is a tier 1 factor. The centrality of village grid management is 3.08 and the driving force is 6, which is a tier 2 factor. The centrality of normative document development is 2.7 and the driving force is 5, which is a tier 3 factor. The centrality of village network construction is 3.54 and the driving force is 9, which is a tier 3 factor. Leadership team structure, village grid management, normative document development and village network building constitute decision-making intersections, which are key catch-alls for the improvement of village emergency response capacity and resilience.

Failure causes of emergency response in oil and gas pipeline accidents and social network analysis

GONG Yunhua, ZHANG Zhe

2025, 35(1): 209-215. doi:10.16265/j.cnki.issn1003-3033.2025.01.1536

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To avoid worsening the consequences of oil and gas pipeline accidents due to emergency failures, the causes of emergency failure in 27 accidents at home and abroad were analyzed using the HFACS model. Based on the results of grounded theory(GT) statistical coding analysis, a classification model of failure causes of emergency response in oil and gas pipeline accidents was proposed. SNA was used to develop the relationship network of the causes of emergency failures in oil and gas pipeline accidents. The core-periphery, centrality, and association direction index analyses were used to identify core factors and factors with high association and strong mediating roles in the classification model of the causes of emergency failures in oil and gas pipeline accidents. The results indicated that the classification model of emergency failure causes in oil and gas pipeline accidents was divided into five levels: government and emergency department factors, operator organizational factors, operator unsafe supervision, preconditions for unsafe behavior of on-site personnel, and unsafe behavior of on-site personnel. The emergency failure causes were further divided into 16 bottom-level factors, among which there were 9 core factors: inadequate safety supervision by government and emergency departments, ineffective emergency rescue, regulations defects, insufficient supervision by pipeline operators, technical environment, and skill errors. Skill errors, regulations or procedure defects, technical environment, and insufficient supervision by operators were highly associated factors. Moreover, pipeline operators' regulation defects, procedure defects, technical environment, insufficient supervision, improper resource management, and decision-making errors were strong mediating factors.

Exploration of regional emergency talent training model and full industry chain service

CHEN Jingxu, LIU Yawen, ZHAO Yongwen, CAO Zhao, LIU Yejiao, LI Shuguang

2025, 35(1): 216-222. doi:10.16265/j.cnki.issn1003-3033.2025.01.0536

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To meet the growing demand for emergency talent in regional industrial development, the "government, industry, academia, research, and application" education model was used to investigate an integrated training model for emergency talent from undergraduate to master and doctoral degrees. Based on the issues of unclear hierarchical demands, core competency assessments, and knowledge system construction in the Western emergency industry, the FP-Growth algorithm was used to mine and divide the association rules between courses and corresponding competencies to evaluate core competency. Safety engineering capabilities were compared to determine the focus of capability training and explore talent training pathways. The industry-specific undergraduate "3+1/1" programs were developed by integrating industry, education, theory, and practice. Moreover, an "order-based" talent innovation training model for master and doctoral levels in government-school-enterprise cooperation was proposed. Finally, the concept of regional emergency talent full industry chain service was proposed by integrating industry-education and science-education efforts to cultivate emergency talent. The results indicated that the core competencies based on the four-tiered talent demand framework were highly associated with the curriculum system. The issues of homogeneity in talent output and competency emphasis were addressed by clarifying the training direction of safety and emergency disciplines from professional focus and application backgrounds. The proposed concept of regional emergency talent full-industrial chain service closely connected educational content with market demands, enhancing students' comprehensive abilities and contributing to the regional closed-loop development of the modern emergency management industry.

Research progress on emergency management of nuclear power plants: bibliometric analysis based on CiteSpace

LIU Zhen, LIU Tao, PENG Guowen

2025, 35(1): 223-230. doi:10.16265/j.cnki.issn1003-3033.2025.01.0495

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To improve the theory and practice of nuclear power plant emergency management, a bibliometric approach was applied using CiteSpace software. A total of 355 relevant journal articles were collected from the Web of Science (WoS) database. By constructing mixed co-occurrence maps of researchers and institutions, keyword co-occurrence maps, keyword clustering maps, and visual timelines of keyword frequency, the research hotspots, focal areas, research forces, development paths, and frontier trends in nuclear power plant emergency management were analyzed in detail. The results indicate that, influenced by the Fukushima nuclear accident, research on nuclear power plant emergency management has gradually increased since 2011, particularly in the areas of risk assessment and decision support. The field of nuclear power plant emergency management research is characterized by its diversity, with a primary focus on accident management, performance monitoring, decision-making, and the application of simulation technologies. The interconnections between these topics fully demonstrate the complexity of nuclear power plant emergency management as an interdisciplinary field. Currently, the nuclear power sector is undergoing rapid development, and the emergency management system and capabilities of nuclear power plants are continuously improving to reduce the risks of potential accidents. Research from the perspective of interdisciplinary collaboration on emergency preparedness, response, and strategies for nuclear power plants has become a significant and emerging research direction.

Numerical simulation of dust suppression characteristics in regeneration of old industrial buildings

TIAN Wei, MA Ruihao, GUAN Xiaojie, GAO Mingzhe, TAN Xiao

2025, 35(1): 231-238. doi:10.16265/j.cnki.issn1003-3033.2025.01.0170

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To explore the effects of dust-generating processes and environmental fluids on the spatiotemporal distribution of dust during the regeneration of old industrial buildings, field studies and numerical simulation was used to investigate the dust suppression characteristics inside the construction area under different processes and background wind speeds. The dust source characteristics in the main processes of re-construction of old industrial buildings were clarified by analyzing a large amount of dust data. Typical old industrial buildings were selected for the field studies of sanding and shoveling processes. Numerical simulation was performed to investigate the dust mass concentration distribution under five inlet air velocities and two dust-producing processes and to identify the dust-aggregation zones in the reclaimed construction space of old industrial buildings. The results showed that the maximum dust mass concentration in the grinding process exceeded the limit by 54 and 37 times in the grinding and shoveling process, respectively. Inlet wind speed affected the dust mass distribution of the main impact zones in the construction area and changed the process of dust reaching dynamic equilibrium. Dust-generating process variations impacted on the dust mass concentration distribution. Moreover, the workload increase of a specific process resulted in a proportional increase in downstream dust mass concentrations. This study can provide fundamental knowledge for dust management in the reconstruction of old industrial buildings.

Model and verification of human thermal reaction under different solar radiation intensity environments

LIU Donghua, WU Wenjie, YANG Jie

2025, 35(1): 239-246. doi:10.16265/j.cnki.issn1003-3033.2025.01.0983

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In order to evaluate the heat strain level of outdoor workers in high temperature environments, a thermal response model was developed based on the Tanabe 65 thermal physiological model and principles of heat transfer by modifying basal metabolic rate and human surface area of the original model. The experimental data in the literature and the Tanabe model were used to verify the effectiveness of the model. The results show that the predicted results of the high temperature human thermal reaction model established under different solar radiation intensities are in good agreement with the experimental results. In an extreme environment of 36 ℃ and high solar radiation intensity, the maximum difference between the simulated value of core temperature and the measured value is less than 0.24 ℃, and the maximum difference between the simulated value of average skin temperature and the measured value is less than 0.3 ℃, which is more accurate than the classic Tanabe model. Thus, the established model can be used to predict human physiological parameters such as human core temperature and skin temperature in hot environments in the presence of solar radiation, and provide reference for the safety assessment of outdoor workers.

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