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Table of Content

    28 June 2024, Volume 34 Issue 6
    Safety social science and safety management
    Study on multidimensional quantification of individual workload of controllers
    WANG Lili, GU Qiuli
    2024, 34(6):  1-9.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1562
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    In order to enhance the efficacious operation of the air traffic control system, a quantitative model was established by focusing on the individual load of controllers. Tests were designed to collect pre-service and post-service data on various indicators from 16 area controllers in the front line. Sensitive variables were selected to describe individual loads based on changes in test data. A comprehensive assessment index system was established that included three dimensions: psychological perception load, physiological reaction load, and mental workload. The controller individual load index model was developed. The optimal weights of the individual load index were determined by the the entropy-critic combination weighting method. The quantitative model of the controller's individual workload was finally derived. Further K-Means clustering analysis was performed based on the controller's individual load composite index. There were evident discrepancies in the workload changes of the controllers due to different individual postures. The results indicate that the post-post individual workload changes of the controllers could be classified into three distinct groups. The first group, comprising 50% of the total number of controllers, exhibited the smallest post-post individual workload growth. The second group, accounting for 43.75% of the total number of controllers, exhibited a moderate post-post individual workload growth. The third group, comprising 6.25% of the total number of controllers, exhibited the largest post-post individual workload increase. These findings align with the instructor's ratings of controller competence.

    Risk zoo (VI): metaphorical traceability, evolution process and research outlook of black swans
    TONG Ruipeng, LIANG Fanjie, MENG Ziyao, WANG Yiyan
    2024, 34(6):  10-19.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0091
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    In order to prevent the risk of uncertainty and extreme impact brought by the black swan, the origin, transmutation and prospect of the black swan were studied in depth under the perspective of metaphor theory. Firstly, the conceptual integration theory was adopted to analyze the cognitive construction mechanism of the black swan risk metaphor from the aspects of historical flow and development trend, summarize the overall development trend of the black swan, and on the basis of this, divide the black swan into three important transmutation stages of founding, exploring development, and innovating and perfecting, so as to put forward the research dilemma and future outlook of the black swan under the background of digital intelligence empowerment. The results show that the risk metaphor meaning of black swan is a risk event with rarity, episodic, unexpected and unpredictable, and extremely negative impact; the development of the black swan risk metaphor will enter the stage of theoretical differentiation of quantitative fluctuation and change, and the direction of the research should be from generalized application to precise application, from single risk to composite risk, and from silo research to group research.

    Study on resilience index of civil aviation airports under meteorological disasters
    QI Lin, HUAI Yongcheng, DAI Kejuan, CHEN Xiaolin, HUANG Xin
    2024, 34(6):  20-28.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1560
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    To study the performance of different airports' resilience under meteorological disasters and the causes of their differences, firstly, the definition of airport resilience based on airport functional level was put forward, which covered three sub characteristics: resistance, robustness and recovery. Then, by calculating the airport functional level under meteorological disasters through flight data, airport resilience index and sub characteristic indices were obtained to reflect the airport's resilience. Finally, taking the disaster of the snowstorm as an example, the distribution pattern of resilience index of affected airports in the United States and the reasons for the differences of resilience index among different airports were analyzed. Furthermore, the performance of the resilience index of the affected airports under the disaster of winter storms, floods, tropical storms and tornadoes was analyzed. The impact of disaster types on airport resilience index was also analyzed. The results indicate that the difference of airport resilience index is mainly caused by the resistance index. The key factors that cause the difference in airport resilience index under snowstorm disaster are throughput, aircraft fuselage maintenance plan level and engine maintenance level. The resilience index of the airport under winter storm, flood and snowstorm is basically the same. The lowest relative difference of the resilience index is 7.519% and 5.521%, while the average relative difference is 23.021% and 21.037%. The calculation method of airport resilience index proposed in this paper can accurately reflect the resilience properties of airports.

    SPO risk evolution based on improved functional resonance analysis method
    SHI Tongyu, MA Yusen, CAO Yujie, FU Yuxiang, WANG Yantao
    2024, 34(6):  29-38.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1428
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    To fully identify the interaction and coupling effects between the subsystem elements in SPO mode, an improved FRAM was developed to propose a quantitative analysis model based on the risk evolution mechanism. Firstly, the fuzzy comprehensive evaluation method was used to evaluate the functional variability of system functional modules. Then, the concept of structural importance was introduced to analyze upstream and downstream coupling variability of functional modules of the computational system and determine the coupling effect mechanism between various functional elements of the system. Finally, the Monte Carlo simulation method was used to calculate the functional resonance risk index for SPO-specific scenarios, analyze potential functional resonance situations, and set effective functional barriers. The results showed that the improved functional resonance analysis method can explain the nonlinear coupling situation of SPO. The functional variability coupling change index of modules such as air traffic control and services, pilot cognitive state, and captain control was relatively high with a value of more than 2.5. In the approach and landing scenario, eight functions (e.g., crew technical training, important meteorological information, air traffic control services, and ground information support) were prone to functional resonance. Combined with the functional resonance results, the physical, symbolic, functional, and invisible functional safety barrier measures were set to provide specific operational suggestions.

    Research on influence of emotion on miners' safety behaviour competence
    ZHANG Qian, LI Jizu, SHEN Min
    2024, 34(6):  39-47.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1742
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    In order to reduce the occurrence of safety accidents in coal mine production, from the perspective of emotion control, based on Valence-Arousal (V-A) emotional model, combined with emotional arousal methods and physiological measurement techniques, a cognitive experiment of miners' safety behavioral competence was conducted. Attention and decision-making time under different emotional states were measured. The regression analysis was used to investigate the continuous effects of degree-of-arousal on attention and risk preference under different emotional valence. The results show that in low degree-of-arousal and positive emotions, decreasing degree-of-arousal leads to weaker attention and more risk aversion in decision making in miners. In the high degree-of-arousal and positive emotions, with the increase of degree-of-arousal, the level of attention and risk aversion of miners in decision-making first increases and then decreases, until it is lower than neutral emotions. In low degree-of-arousal and negative emotions, decreasing degree-of-arousal would make miners pay less attention and have lower risk aversion in decision-making. In the high degree-of-arousal and negative emotions, an increase in degree-of-arousal increases and then decreases the attention and risk aversion in decision making, even until they are lower than the level of neutral emotions. By contrast, in the high degree-of-arousal range, increasing degree-of-arousal in positive emotion is more likely to reduce miners' safety behavioral competence to lower than the level of neutral emotional.

    Safety engineering technology
    Response determination of multi-story frame-rocking wall structure under non-stationary random seismic excitation
    SONG Yinghua, MA Jian, ZHANG Yuanjin
    2024, 34(6):  48-56.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0987
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    Frame-rocking wall was a composite self-resetting structure that could effectively improve the seismic resistance and toughness of buildings. To fully understand the random response characteristics of structures under earthquakes, a simplified nonlinear equation for a multi-degree-of-freedom frame-rocking wall structure was constructed, and an equivalent linear dynamic equation with time-varying parameters was constructed based on the assumption of pseudo harmonic behavior in response using equivalent linearization. Further, based on the principle of random averaging, the Fokker-Planck-Kolmogorov(FPK) equation could be derived to determine the time evolution form of probability density function(PDF) for controlling the amplitude of the response, and ultimately the first-order differential equation for the time-dependent variance of the random response could be obtained. Finally, a computational model was constructed using a framework of a certain teaching building as a sample for validation. The results show that the approximate analytical method has excellent accuracy, and while ensuring the accuracy of the random response time-related variance, it can improve the efficiency of analysis compared to traditional Monte Carlo simulation(MCS) methods. In the results of non-steady ground motion power spectrum models in separable and non-separable forms, the trend of the random response variance curve is related to the form of random seismic excitation, and its segmentation points show obvious unsmooth phenomena under the action of segmented modulation of non-stationary spectra. The results under different types of random seismic excitation disturbances demonstrate the excellent applicability of this method.

    Multi-objective prediction optimization for large-diameter slurry shield tunneling construction based on CatBoost-MOEAD
    WU Xianguo, LIU Jun, SU Feiming, CHEN Hongyu, FENG Zongbao
    2024, 34(6):  57-64.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1734
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    To effectively optimize the shield construction parameters and achieve the goals of safety, efficiency, and energy-saving in the large-diameter slurry shield tunneling process, a hybrid intelligent algorithm combining categorical boosting (CatBoost) and decomposition was proposed based on a multi-objective evolutionary algorithm (MOEAD). The main shield construction parameters were set as the major research objects considering shield construction parameters and geological conditions, and the surface settlement, penetration rate, and tunneling-specific energy were determined as the prediction and control objectives. Moreover, the selected shield construction parameters were optimized, and a line of Wuhan rail transit was used to validate the hybrid algorithm performance. The results showed that the proposed CatBoost algorithm had great prediction performance for large-diameter slurry shields with the fitting accuracy (R2) of the three control objectives more than 0.9. The model's importance rank indicated that the total propulsion force and propulsion speed of the large-diameter slurry shield had significant influences on surface settlement, penetration, and tunneling-specific energy. The proposed CatBoost-MOEAD hybrid intelligent algorithm had an obvious optimization effect on the three control objectives, and the optimization ranges of surface settlement, penetration rate, and tunneling-specific energy reached 12.35%, 7.47%, and 10.70%, respectively. Moreover, the control ranges of corresponding shield construction parameters were presented.

    Simulation study on heat transfer of thermal runaway lithium-ion battery in confined space
    ZHANG Qingsong, JIA Yan, ZHAI Qiyue, LIU Tiantian
    2024, 34(6):  65-72.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0945
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    To investigate the source and proportion of thermal runaway heat transfer of lithium-ion batteries in confined conditions space such as aviation transportation, the 18650 lithium-ion battery (100% state of charge) with lithium cobalt oxide (LCO) as the cathode material was used. The heat transfer model of thermal runaway of lithium-ion batteries was proposed by ANSYS Fluent software. Furthermore, the pyrolysis gas generated by the first battery and its thermal runaway was used as the heat source, and the second battery was heated to thermal runaway through radiation and convection. The results showed that when 2nd battery reached the thermal runaway temperature, the heat generated by the internal side reaction accounted for 30.01% of the total energy. The gas combustion generated by the 1st battery thermal runaway provided energy for 2nd battery thermal runaway, accounting for 5.64% of the total energy. When 2nd battery reached the maximum temperature, the heat generated inside the battery accounted for 87.39%, and the energy provided by the gas combustion was 1.76%. the pyrolysis gas combustion accelerates 2nd battery's thermal runaway, though it is a heat source, it is not a heat source. Although the combustion of pyrolysis gas accelerated the thermal runaway process of 2nd battery, the proportion of energy provided was relatively small.

    Mechanical analysis of buried pipeline collapse process based on unit life and death technology
    TENG Zhenchao, ZHOU Yadong, CHI Linlin, LIU Xiaoyan, LI Zhengwei, LIU Bo
    2024, 34(6):  73-81.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1616
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    In order to solve the nonlinear problem of dynamic loss of soil around the pipe during the collapse development process of the buried pipeline, first, a numerical analysis method was used to construct a nonlinear coupling model of pipe-soil that passes through the collapse zone. Then, the model was verified based on experimental measured data and specifications. Finally, a study on the damage mechanism of buried pipelines subjected to soil collapse was carried out, and the dynamic evolution process and mechanical characteristics of buried pipeline collapse were discussed. The results show that the axial stress is the control stress, the mid-span pipe bottom is the control point, the mid-span section is the dangerous section, and excessive tensile stress is the main reason for failure of buried pipelines. It is confirmed that "pipe-soil separation" phenomenon exists. When the collapse depth reaches 48 mm, "pipe and soil separate", as the collapse process progresses, the entire process of pipe-soil structure from the beginning of deformation to the tensile failure of buried pipeline can be divided into three stages: the top pressure stage, the transition stage and in the bottom tension stage, the collapse depth of 80 and 160 mm is the dividing point. When the collapse depth reaches 59 mm, the pipe top stress changes from valley to peak at mid-span position. When collapse depth reaches 80 mm, friction stress appears.

    Influence study on inhibitory effect of porous materials on LPG explosion in vent pipe
    GUAN Wenling, HOU Yifei, REN Changxing, DONG Chengjie, ZHANG Wang
    2024, 34(6):  82-89.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1139
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    In order to investigate the influence of porous materials on the explosion propagation characteristics of LPG in vent pipes, experiments were conducted on a self-built gas explosion test platform. The inhibitory effect of silicon carbide materials with different thicknesses and porosities on the flame propagation characteristics and explosion overpressure in the vent pipe was investigated. The results show that porous materials can effectively prevent the propagation of LPG explosion flame. With the increase of porosity and thickness, the propagation distance, propagation speed and intensity of LPG flame are reduced, and the porosity has a greater effect on the fire resistance than the thickness. The installation of porous materials also reduces the maximum explosion overpressure. When LPG is not ignited behind the installation location, increasing the porosity and thickness of porous materials, the maximum explosion overpressure in front of the material (PT1, PT2) increases, and that behind the material (PT3) is close to the static film-breaking pressure. The firestopping effect of material porosity and thickness shows that increasing the porosity of the material for firestopping should be prioritized when the strength of the material is up to standard in engineering applications.

    Multi-scale attention feature-enhanced fusion of a new network for infrared small object detection
    JIA Guimin, CHENG Yu, QI Mengfei
    2024, 34(6):  90-98.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1565
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    In order to improve the performance of small target detection in infrared imaging and the ability of low altitude airspace supervision, an infrared small target detection network based on multi-scale attention feature enhancement fusion was proposed. Firstly, Resnet34 was used to extract the multi-scale features of infrared images. Secondly, the multi-scale spatial attention feature enhancement module(MFEM) was used to improve the ability of feature extraction. Then, in the step-by-step up sampling process, the dual channel attention feature fusion module(DFFM) was used to fuse the semantic information and detail information to better protect the characteristics of infrared small targets. Finally, taking the video sequence detection of ground/air infrared dim small aircraft target as an example, the real scene test was carried out by comparing with other methods. The results show that compared with existing methods, the proposed method improves the scores of intersection over union(IoU), F-measure and false negative rate(FNR), and can accurately locate the target and generate good segmentation results. The DFFM can simultaneously use multi-scale context information and spatial attention mechanism to highlight infiared small targets. The DFFM assigns weights to sets of different channel features, thereby obtaining the most appropriate feature map for feature fusion and improving the detection performance.

    Drilling risk analysis approach based on historical grey information of adjacent wells
    WEI Kai, WANG Chenyang, WU Desheng, GUO Yong, LIAO Hualin
    2024, 34(6):  99-108.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1575
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    To address the low prediction performance of drilling risks in unknown formations during oil and gas development, a drilling risk assessment approach based on grey adjacent well information was proposed. Firstly, a three-parameter interval grey number was proposed to present formation information. Secondly, the depth adjustment and weighted interpolation methods were used to develop a target well gray wellbore information matrix based on adjacent well information. Subsequently, a three-parameter grey safety drilling fluid density window was proposed. Then, a risk assessment model including overflow, well leakage, collapse, and adhesion was developed based on generalized strength-interference theory. Finally, a case study was performed on five wells in an oil field in western China. The results indicated that introduced concept of wellbore information matrix and optimization parameter description of depth adjustment and weighted interpolation effectively presented the profile of the target well's wellbore information. The grey safe drilling fluid density window developed by three-parameter interval grey numbers and wellbore information matrix significantly reduced uncertainty in drilling risk assessment. The evaluation results from drilling risk assessment model based on generalized strength-interference theory agreed well with that in real situations, providing reliable fundamental knowledge for drilling risk assessment.

    Pull-out model test of anti-floating multi-bell-shaped anchor under cyclic loading
    CHEN Chen, YU Jie, LIU Zhe, XIE Shasha, YI Chengcheng
    2024, 34(6):  109-118.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0081
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    In order to study the reasons for the deterioration of the anchor performance caused by groundwater variation, a series of physical model tests were employed to explore the anchorage enhancement of the expanded anchor in the anti-floating process. Firstly, a single pull-out test was carried out through the indoor model test, and the change of bearing capacity of multi-bell-shaped expanded anchor under different buried depths during the pull-out process was obtained. Secondly, according to the ultimate uplift bearing capacity obtained from the single pull-out test, the cyclic test was carried out to explore the evolution mechanism of the bearing performance of the multi-bell-shaped expanded anchor under different cyclic amplitudes, cyclic times and cyclic frequencies. Finally, the image particle velocity method (PIV) was used to analyze the deformation mechanism of the surrounding soil, and the variation characteristics of the surrounding soil displacement under single pulling-out and cyclic loading were obtained. The test results show that the axial load-displacement curve of the bell-shaped expansion anchor can be roughly divided into three stages: elasticity, vibration and failure. With the increase of buried depth, the ultimate bearing capacity and soil displacement of the anchor increase. Under the action of cyclic loadings, the increase of the cyclic load ratio, the number of cycles and the cycle frequency will weaken the bearing capacity of the anchors.

    Dentification of leakage pressure drop rate signal of trunk gas pipeline based on SVM
    WU Xia, CHEN Honghuan, JIA Wenlong, SUN Yibin, REN Sibo
    2024, 34(6):  119-126.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1137
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    In order to solve the problem that the pressure drop signals caused by compressor suction or upstream block valve cut-off conditions leaded to incorrect shut-off of the block valve, and the problem that the block valve failure due to insignificant pipeline pressure drop caused by small hole leakage, a simulation model was established. Taking a typical gas transmission trunk line as the research object, 300 sets of pressure drop signals under three different working conditions, namely compressor suction, emergency cut-off of the block valve and pipeline leakage, were obtained. The pressure drop rate of the pressure drop signal was calculated by point-to-point detection method. Singular value decomposition(SVD) method was used to extract the characteristics of the pressure drop rate signal, and the min-max normalization method was used to normalize the characteristic values of the pressure drop rate signal. SVM method was used to identify the characteristic value signals of different pressure drop rates, and the corresponding working conditions were obtained. To solve the problem that the unreasonable setting of kernel function parameters and penalty factors in the SVM model affected the accuracy of algorithm recognition, TLBO algorithm was used to optimize the kernel function parameters and penalty factors, and a TLBO-SVM model for intelligent identification of gas pipeline leakage signals was established. The model was applied to classify and identify 300 groups of simulated pressure drop rate signals in three working conditions. The results show that the recognition accuracy of the model is 92.22% for three kinds of pressure drop rate signals under different working conditions. The identification accuracy is 96.67% for small hole leakage with a leakage diameter of 50-125 mm and a pressure drop rate range of 0.01-0.07 MPa/min. For the actual leakage pressure drop rate signal of a main pipeline, the accuracy of TLBO-SVM is 100%.

    Machine learning-based recognition for recognizing operating conditions of multi-product pipelines
    LI Miao, LI Lingbo, ZUO Zhiheng, ZHANG Li, JIANG Luxin, SU Huai
    2024, 34(6):  127-135.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1410
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    In order to solve the problems that some operating conditions could not be automatically identified and the accuracy of abnormal operating condition recognition was low in the process of monitoring the production and operation of multi-product pipeline system, the intelligent operating condition recognition method was applied to construct a multi-product pipeline operating condition recognition model with real-time monitoring capability. First, logic rule discrimination methods and event logs in the multi-product pipeline system were used to supplement the data labels. Second, the data were segmented according to the start and end time of the operating conditions, and the subsequence of different operating conditions were extracted by using the sliding window. Third, the features of subsequence were extracted to construct the model for operating condition recognition of multi-product pipelines, and the recognition effects of six classification models, namely, random forest (RF), adaptive boosting (AdaBoost), support vector machine (SVM), time series forest (TSF), random interval spectral forest (RISF) and sequence learner (SEQL), were compared and analyzed. Finally, a real multi-product pipeline was used as an example for model validation. The results show that the TSF model has the highest recognition accuracy for the four operating conditions of valve switching, valve internal leakage, pigging and sling pump, and is more suitable for the recognition of short-term operating conditions. In contrast, the recognition precision of the AdaBoost model has a higher probability of including the true value in the 95% confidence interval.

    Mechanical properties and failure characteristics of coal rock combinations with different inclination angles under uniaxial compression
    DU Feng, WANG Kai, SUN Jiazhi, DENG Yun, FENG Chengtao, XIE Chenxiaoxian
    2024, 34(6):  136-145.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0785
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    In order to explore the mechanism of rock burst induced by the instability of coal rock combinations under different dip angles, a particle discrete element program was used to conduct uniaxial compression tests on five sets of coal and rock combination models with different dip angles: 0, 15, 30, 45 and 60°. The research results indicate that coal is the main cause of instability and failure in coal-rock combinations. When the dip angle of the coal-rock combination increased from 0° to 30° and from 30° to 60°, the uniaxial compressive strength of the coal-rock combination decreased by 2.01% and 9.59%, and the number of microscopic cracks decreased by 22.9% and 4.0%, respectively. The appearance time of the acoustic emission signal is advanced, indicating that the increase in dip angle led to a decrease in the uniaxial compressive strength of the coal-rock combination, and the instability failure time is advanced, but the degree of failure is reduced. In the early stage of uniaxial loading, the movement of coal and rock particles at the interface of different dip angle combinations led to an expansion trend at the interface. The dip angle affected the movement of coal and rock particles near the interface, leading to a gradual transition of the failure area of the combination from the coal body to the coal-rock interface. When the cracks in the coal body extend to the coal-rock interface, the interface slip effect generated by the high dip angle coal-rock combination causes its failure mode to change from compression shear failure to slip failure, with a 30° dip angle as the boundary.

    Experimental study on tensile and compressive mechanical properties of coal samples under influence of water content
    ZHANG Meichang, BAI Chenhao, QI Yun, WANG Wei, XUE Kailong, REN Jiewei
    2024, 34(6):  146-156.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0104
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    In order to reveal the effect of water content on the tensile and compressive mechanical properties of coal samples, coal samples from the No.22 seam of the Hanjiawa coal mine were selected. Brazilian disc splitting tests, uniaxial compression tests, and triaxial compression tests were conducted on coal samples with different moisture contents and confining pressures to explore the mechanical characteristics of water containing coal samples. The results show that the strength and elastic modulus of coal samples decreases exponentially with increasing water content. When the coal samples reach saturation, the strength and elastic modulus decrease by 48.3% and 37.6% respectively. With the increase in water content, the plasticity of coal samples is enhanced, and the samples are prone to breakage. Under triaxial compression, the peak value of failure and the strength of coal samples increase with the increase of confining pressure. However, the strength of water-bearing coal samples under different confining pressures is obviously lower than that of dry coal samples. The water content has little effect on the internal friction angle of coal samples but has a significant effect on the cohesive force of coal samples.

    Study on key tactical factors of container security threats based on IPFS-DEMATEL-ISM Method
    SHENG Jianqiao, ZENG Lifan, FANG Yuan, WU Jun
    2024, 34(6):  157-163.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0074
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    In order to address the increasingly serious cloud-native container security threats arising from large-scale cloud migration of systems, the ISM method merging IPFS, DEMATEL, and method were proposed to identify the key tactical factors influencing cloud-native container security threats and their hierarchical logical relationships from the security intruder perspective. The findings of this research are as follows: the centrality and causality of the persistence and privilege escalation tactical phases are high, positioning them at the core of the entire cloud-native security threat landscape. Security attacks during these two phases require high-priority attention. Threat attacks during the execution and persistence tactical phases constitute essential factors in cloud-native container security. The threats during the initial access, credential theft, and lateral movement tactical phases have the most direct impact on cloud-native container security. In comparison with traditional and triangular fuzzy sets improved DEMATEL-ISM, our proposed method has better performance in identifying container security-related critical factors.

    Vehicle trajectory prediction based on EKF-GRU
    ZHANG Chuanying, XU Guoyan, CHEN Zhifa, ZHOU Bin, CHEN Liwei, HONG Wei
    2024, 34(6):  164-172.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0720
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    To enhance the driving safety and achieve correct decision planning for autonomous vehicles, a safe driving trajectory prediction method based on EKF-GRU was proposed. By combining learning-based methods with physics-based approaches, the prediction accuracy was improved and the rationality of the predicted trajectories was enhanced. In the first step of this method, a prediction network was constructed based on GRU to predict the longitudinal acceleration and yaw angular velocity of vehicles by extracting historical trajectory features. In the second step, an EKF state estimator was built based on the nonlinear vehicle kinematics to generate the vehicle's future limited-time trajectory, incorporating the observations obtained previously. The trajectory prediction method was validated on the NGSIM I-80 and US-101 multi-vehicle trajectory datasets. Experimental results demonstrate that the final distance errors (FDE), root mean square errors (RMSE), and average distance errors (ADE) of the predicted trajectories generated by traditional physics-based methods are 6.48, 7.69 and 3.03 meters, respectively. In contrast, trajectories predicted using EKF-GRU exhibit higher accuracy, and the corresponding values are 5.45, 6.67 and 2.56 meters, respectively. This represents improvements of 15.90%, 13.26% and 15.51%.

    Correlation and traceability analysis of hazardous chemical explosion accidents based on Bayesian network
    JIANG Zijian, ZHOU Rongyi, SHI Yunxiao, LIU Can, YANG Bifan, ZHENG Shiqiu
    2024, 34(6):  173-180.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1848
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    In order to improve the effectiveness of hidden danger investigation, a reasoning model for hidden danger of explosive accidents of hazardous chemicals was proposed based on BN. First, according to the theory of combustion and explosion and expert experience, the main types of hidden dangers affecting the explosion of hazardous chemicals were determined, and BN model of explosion, leakage and ignition source was constructed. The prior and conditional probabilities of hidden dangers were determined by triangular fuzzy numbers. Then, Bayesian forward reasoning was used to calculate the probability of explosions of hazardous chemicals. Combined with reverse reasoning, the hidden dangers that were most likely to lead to explosion accidents were identified, and the formation of hidden risk factors was traced back. Finally, Bayes sensitivity analysis method was used to determine the key hazards affecting the explosion of hazardous chemicals and the correlation between the accident hazards, and it was verified by an explosion case of oil storage tank. The results show that the explosion probability of the storage tank calculated by causal reasoning is about 4.7%. Equipment overpressure and electric spark have the highest posterior probability, which is the most likely to lead to the explosion of the storage tank. The reason can be traced back to the failure to inspect the storage tank regularly or the failure to use explosion-proof equipment. Electrical spark and valve or flange damage are the key hidden dangers of oil storage tank explosion, and the accident hidden danger is related to the chain of "valve or flange damage → abnormal oil gusher → leakage exceeding limit → fire source (electrical spark) → explosion".

    Public safety
    Experimental study on evacuation trough bottleneck under typical optimization strategies
    LUO Lin, QIN Tianyu, YANG Gaobo, YAN Zhengyi, FU Zhijian
    2024, 34(6):  181-187.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1912
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    In order to examine the effects of typical optimization measures on the efficiency and safety of evacuating bottlenecks, evacuation tests incorporating pedestrian characteristics were conducted. The test encompassed 28 distinct cases, representing different combinations of optimization measures and pedestrian traits. Parameters such as evacuation time, speed, and local occupant density were measured across all conditions. Our findings reveal that the efficacy of bottleneck optimization measures is influenced by factors such as bottleneck width, the presence of luggage, and fixed evacuation directions. Practical implementation needs a tailored approach, integrating pedestrian characteristics and site-specific control strategies. Specifically, introducing a column in front of the bottleneck significantly benefits pedestrians without luggage, leading to a 15.30% reduction in density during bottleneck navigation with narrower widths, thereby enhancing safety, and concurrently improving evacuation efficiency by 13.18% in scenarios with wider bottleneck widths. Meanwhile, introducing a rail is preferable for pedestrians carrying luggage with wider bottleneck widths, especially when combined with a fixed evacuation direction, significantly enhancing evacuation efficiency by 21.90% while maintaining safety. Among the three bottleneck configuration alterations, incorporating a funnel-shaped passage preceding the bottleneck stands out as the most effective optimization measure, resulting in a notable 9.59% reduction in density, thereby enhancing safety, along with a simultaneous 9.14% decrease in evacuation times. It is noteworthy that the implementation of a straight channel or the combination of a straight channel and a funnel-shaped passage may yield negative impacts on both safety and efficiency.

    Comprehensive safety evaluation method for multifunctional reserve general warehouse
    CUI Pengcheng, XU Yu, CHEN Xin, ZHANG Tao, WANG Jin
    2024, 34(6):  188-196.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1822
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    To effectively assess the various risks faced by the multifunctional reserve general warehouse, a comprehensive safety evaluation method for the multifunctional reserve general warehouse was proposed. First, the integrated risk characteristics of dynamic-static unity, subjective-objective unity, and direct-indirect unity for multifunctional the reserve general warehouse were summarized. Next, based on the combination of a static risk index system and a dynamic risk assessment model, a method for measuring the triggering effects and calculating the residual risks of the multifunctional reserve general warehouse was proposed. Lastly, safety evaluation criteria were delineated. Taking the core area of a logistics hub in China as the application case, the safety risks of the selected district were evaluated. The applicability and shortcomings of the proposed comprehensive evaluation method for the multifunctional reserve general warehouse were discussed. The results indicate that the method can effectively reflect the risks and their changing trends of the studied warehousing system. The practical application demonstrates that it is crucial to determine the trigger relationships and residual risks for accurately characterizing and evaluating the safety status of the multifunctional reserve general warehouse.

    Emergency technology and management
    Facility location of material reserve bases for large railway projects under uncertainty
    ZHANG Jin, YANG Wenguang, SUN Wenjie, SHEN Hao, HONG Zhichao, LI Guoqi
    2024, 34(6):  197-206.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0814
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    In order to improve the reliability of the logistics facility network of railway construction projects in complex environments, scenario reduction techniques were used to generate a minimum subset of disruption scenarios and their disruption probabilities to describe the disruption scenarios of transport channels. The polyhedral uncertainty sets were used to describe the uncertainty of logistics demand. To minimize the combined costs of transport, construction, operation and penalty costs, a two-stage stochastic and robust optimisation technique was applied to construct an uncertainty optimisation model for the location of material reserves bases. The model was solved based on a C&CG algorithm. The validity of the model and the algorithm was verified by taking a C railway construction project in a complex environment as an example. The results show that the cost variation coefficient of the model-acquired solutions is 4.3% of the traditional model in the random disruption scenario, and the cost fluctuation of the model-acquired solutions can be up to 38% of that of the traditional model in the extreme demand fluctuation. The two-stage uncertainty optimisation model given in this paper can effectively reduce the cost variation of the logistics facility network resulting from the disruption of transport channels and demand fluctuations.

    Study on air traffic controllers' situational awareness reliability in emergency scenarios
    WANG Yanqing, WU Siyu, YANG Keyue
    2024, 34(6):  207-215.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1780
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    In order to improve air traffic controllers' emergency response ability, a reliability model of air traffic controllers' SA was constructed from three aspects: air traffic controllers' personal ability, air traffic control task characteristics and air traffic control equipment. Tower control simulation software and SA Global Assessment Technique (SAGAT) were used to measure the air traffic controllers' SA level in emergency situations. Based on BN, SA reliability was quantitatively analyzed to predict air traffic controllers' level of SA, and based on Bayesian inference, the key factors affecting the reliability of air traffic controllers' SA were analyzed. The results show that the reliability of SA of air traffic controllers is positively correlated with the level of SA in emergency scenarios, and the SA level can be predicted by air traffic controllers' SA reliability. The BN inference analysis reveals that the factors with a higher degree of influence and sensitivity to the reliability of controllers' SA were the deployment of aircraft availability time, the accuracy of the control equipment and the controller's memory. The causal chain that has the greatest impact on the air traffic controllers' SA reliability is the deployment of aircraft availability time→mission characteristics→SA reliability.

    Knowledge graph analysis of hot spots and trends in emergency social mobilization research
    ZHANG Xiaobing, CHEN Zhe
    2024, 34(6):  216-224.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1728
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    In order to provide reference direction for the research of emergency social mobilization more accurately, taking 296 research papers on social mobilization for emergencies included in Chinese Social Sciences Citation Index(CSSCI) journals in China National Knowledge Infrastructure(CNKI) as samples, this paper comprehensively used bibliometrics, knowledge graphs and other visual analysis methods to analyze the number, distribution, institutions, research topics, hot spots and changing trends of papers in this field over the years. The research shows that since 2003, the number of papers published in the study of social mobilization for emergency response has experienced three stages of development, with the increasingly wide distribution of periodicals and loose publishing institutions. There are more and more research topics, mainly focusing on the trigger scenario, implementation mechanism and functional effect of emergency social mobilization in the emergency response stage. Hot topics are constantly emerging with more breadth and depth, and the research methods are relatively limited. There is much room for further research and development, which can focus on the research contents of multi-methods, the whole process and intelligence of emergency social mobilization.

    Empirical study on virtual teaching of fire emergency escape in subway station
    YANG Siwen, DAI Qinling, XU Baili, QI Mengmeng
    2024, 34(6):  225-234.  doi:10.16265/j.cnki.issn1003-3033.2024.06.0258
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    In order to explore the influence of different learning scenarios on the learning effect of learners in the teaching process of fire emergency fire escape in subway stations, and explore the advantages of virtual reality learning. Questionnaires and MP160 physiological instrument were used to obtain different behavioral response data of learners in paper learning, mobile video learning and virtual reality learning scenarios, and these data were processed and analyzed. The results show that there are significant differences in learners' academic performance under the three learning scenarios. Compared with mobile video learning and paper learning scenarios, virtual reality learning scenarios can promote learners' understanding of subway fire escape knowledge and significantly improve academic performance. Under the three learning scenarios, there are significant differences in learners ' interest in learning. Virtual reality learning scenarios show significant advantages in improving learners ' interest in learning subway fire escape knowledge. The virtual reality learning scene reflects a higher level of physiological arousal in terms of physiologic index.

    Occupational health
    Recognition of fatigue state of high-speed rail dispatchers based on EEG signal characteristics
    ZHANG Guangyuan, DENG Long, WANG Yawei, SUN Ziwei, LI Sha, CHEN Cheng
    2024, 34(6):  235-246.  doi:10.16265/j.cnki.issn1003-3033.2024.06.1674
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    In order to enhance the stability and safety of railway driving and effectively identify the influence of the dispatcher's fatigue state on the driving organization, a method for identifying the fatigue state of the dispatcher was proposed based on the characteristics of EEG signals. The fatigue state of the dispatcher was divided according to the working time period, and the high-speed rail scheduling simulation experiment was designed to collect EEG data. The three types of brainwave frequency-domain amplitudes of high-speed rail dispatching subjects were extracted as the characteristic value by wavelet series expansion and Fourier transform, and the classification results of fatigue state were verified by combining the operation characteristics and EEG signal characteristics of dispatchers. The ResNet18+SoftMax model and MobileNet V2+SoftMax model were built through the Python language environment. The input features were converted into a three-dimensional rectangular model based on deep learning. The weights were optimized and adjusted to obtain the optimal model, so as to judge the fatigue state of high-speed rail dispatchers. The research results show that the fatigue state recognition accuracy of the participants in the high-speed rail scheduling experiment by ResNet18+SoftMax and MobileNet V2+SoftMax two models is 92.78% and 99.17%, respectively, compared with support vector machines(SVM) model to improve the awake state and fatigue state recognition accuracy, and reduce the model computing time. Among them, the MobileNet V2+SoftMax model can better identify the fatigue state of the dispatcher. With the principle of MobileNet V2+SoftMax model as the core, the potential fatigue risk of high-speed rail dispatchers under long-term working conditions can be identified more quickly and accurately.