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    28 August 2026, Volume 36 Issue 8
    Safety Science Theories and Methods
    Constructing an independent knowledge system of safety science in China: discipline, academia, and discourse
    Tong Ruipeng, Li Xin, Wang Leyao, Zhao Yunhao, Xu Surui
    2026, 36(8):  1-10.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0188
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    To address the construction of China's independent knowledge system in safety science, this study employed literature analysis and theoretical construction methods to interpret the connotation and development context of the discipline system, refined the theoretical accumulation and paradigm evolution of the academic system, and analyzed the generation logic and hierarchical structure of the discourse system. The results indicate that the discipline system exhibits interdisciplinary characteristics, evolving through four stages—embryonic exploration, initial establishment, rapid development, and cross-disciplinary innovation—forming a full-chain spectrum of source-prevention-control-rescue-management. The academic system is rooted in the organic unity of theory and practice, with paradigm shifts from passive response to proactive prevention, and from human-physical defense to intelligent protection. It features vertical coherence, horizontal integration and dynamic evolution. The discourse system follows cultural, theoretical, and practical generation logic, forming a four-tier structure of value, theory, institution, and practice, with effective integration between local leading theories and international academic expression. Based on the above analysis, the construction path of the independent knowledge system is proposed from the knowledge, practice and subject dimensions. The discipline system provides comprehensive planar support, the academic system runs through the theoretical mainline, and the discourse system forms subject-oriented communication carriers. The three parts complement each other in a point-line-plane structure and jointly build the three-dimensional architecture of China's independent knowledge system of safety science.

    Study on impact of cockpit hierarchy on airline pilots' situational awareness
    Wang Lei, Bai Yu
    2026, 36(8):  11-20.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0841
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    A reasonable cockpit hierarchy was considered conducive to improving the overall performance of flight crews. This study was aimed at investigating the impact of cockpit hierarchy on the SA of airline pilots. First, 30 airline pilots were recruited as participants. By introducing "risky" flight instructors as crew partners, different cockpit hierarchy conditions were established by integrating participants' flight experience, power distance orientation, and role assignment. Second, in a full flight simulator, a low-visibility approach task was performed once by each participant as both Pilot Monitoring (PM) and Pilot Flying (PF). Meanwhile, participants' situational awareness levels were measured through a subjective scale and objective eye-tracking data (fixation and visit indicators). The results show that flight experience has a significant main effect on SA. The interaction between role assignment and power distance orientation has a significant main effect on visit counts. The interaction between flight experience and power distance orientation has a significant main effect on fixation counts. The three-way interaction among flight experience, power distance orientation, and role assignment has a significant main effect on the percentage of fixation counts. Finally, the influence mechanism of hierarchy elements on pilots' situational awareness is revealed. Empirical evidence for optimizing the optimal crew composition under cockpit hierarchy is provided. When acting as PF, participants' fixation and visit mainly focus on the Primary Flight Display (PFD); when acting as PM, they mainly focus on the navigation display (ND), engine/warning display (E/WD) and outside of the window (OTW).

    Effect of human-intelligent system cognitive fit on human-machine collaborative decision-making performance
    Niu Lixia, Li Bo, Li Guo, Pan Lihang, Zhang Guojian
    2026, 36(8):  21-28.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0813
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    To address constrained human-machine collaborative decision-making performance caused by cognitive differences between operators and intelligent systems in coal mine intelligent monitoring tasks, this study, grounded in cognitive load theory, develops a theoretical model of the effects of human-intelligent system cognitive fit on human-machine collaborative decision-making performance. Task complexity, including information load and task structure, and system transparency, including information transparency and process transparency, are introduced as contextual moderators. Using a multi-stage questionnaire survey, empirical data were collected from frontline coal mine workers. The results show that human-intelligent system cognitive fit has a significant positive effect on human-machine collaborative decision-making performance (β=0.253, p<0.001). Information load, task structure, information transparency, and process transparency all significantly and positively moderate the relationship between cognitive fit and human-machine collaborative decision-making performance. Specifically, higher levels of these contextual factors strengthen the positive effect of cognitive fit on collaborative decision-making performance. This effect is particularly pronounced under conditions of high information load and high process transparency.

    Identification and coupling study of risk factors for aviation safety occurrences based on active learning
    Li Li, Xing Ruijie, Bao Suiyi
    2026, 36(8):  29-36.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1066
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    To explore the coupling relationship between risk factors of aviation safety occurrences, AL strategies were introduced into risk factor identification based on 22 439 occurrence text records, and a risk factor identification model combining Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Gated Recurrent Unit (BiGRU) was constructed to achieve automatic identification of risk factors. On this basis, the N-K model was applied to perform a coupling analysis of aviation safety occurrence risk factors. The results show that with 18 000 training samples, the AL strategy reduces manual annotation costs by 70% (12 600 samples), while the model performance (F1=0.907 5) only decreases by 1% compared with the fully data-trained BERT-BiGRU model. The results of the risk factor coupling analysis show that in combinations with high coupling risk values, human, aircraft, and the environment are the key risk factors that cause aviation safety occurrences. As the number of factors involved in the coupling increases, the risk coupling value generally shows an upward trend, with the coupling value for the six factors being the highest at 0.371. In civil aviation safety management, it is necessary to avoid the combined effects of multiple factors.

    Construction and validation of conceptual model of influencing factors of miners' unsafe behaviors
    Liu Jiao, Meng Yuhui, Hao Limin, Wang Lulu, Tong Ruipeng
    2026, 36(8):  37-45.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1906
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    To effectively prevent UB among miners caused by occupational burnout and enhance mine safety production levels, this study employed a mixed-methods approach based on SOR theoretical framework to systematically analyze the causes and prevention strategies of miners' UB. First, integrating in-depth interviews and questionnaire survey data, the mechanisms through which occupational burnout and risk perception influenced UBs were examined from four dimensions: individual, equipment, work environment, and organizational management. Subsequently, a theoretical model was constructed incorporating these four dimensions, occupational burnout, risk perception, and UB. Finally, structural equation modeling was conducted using Smart PLS 4.0 software, and targeted prevention recommendations were proposed for regulatory agencies, mining enterprises, and miners based on the empirical results. The findings indicate that occupational burnout acts as a critical mediating channel through which risk perception affects UB, and all four dimensions of job burnout exert their impacts on UB entirely via this mediator. Individual factors, equipment, work environment and organizational management not only directly predict job burnout, but also indirectly affect UB through the mediating path of job burnout. Meanwhile, equipment, work environment and organizational management can directly affect risk perception and further exert indirect effects on UB through the mediating effect of risk perception (p < 0.05).

    Influence mechanism of self-efficacy on safety behavior of construction safety management personnel
    Wang Lin, Su Yikun, Zheng Zhizhe
    2026, 36(8):  46-54.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0522
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    To improve the safety behavior of safety management personnel in the construction industry, the influence mechanism of self-efficacy on safety behavior was explored. The relationship among self-efficacy, safety motivation, and safety knowledge were also clarified. Based on social cognition theory and self-determination theory, safety motivation and safety knowledge were introduced as mediating variables. Research hypotheses were proposed, and a theoretical model was constructed. A questionnaire survey was conducted using the Self Efficacy Scale, Safety Motivation Scale, Safety Knowledge Scale, and Safety Behavior Scale. A total of 287 valid responses were obtained. Empirical analysis and hypothesis testing were conducted using SPSS and AMOS. The results show that self-efficacy has an indirect effect on safety behavior through the chain mediating effect of safety motivation and safety knowledge. An indirect effect is also produced through the independent mediating effect of safety knowledge. Self efficacy has significant positive effects on safety motivation and safety knowledge. Safety motivation and safety knowledge have significant positive effects on safety behavior. The safety behavior of safety management personnel can be effectively improved by enhancing their self-efficacy, safety motivation, and safety knowledge. The level of safety management can also be optimized.

    Safety Technology and Engineering
    Mine microseismic identification based on multi-scale residual convolutional neural network
    Chen Xiaohong, Cai Chengcheng, Liu Xiaoliang, An Qingxian
    2026, 36(8):  55-64.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1863
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    To address the problems of low recognition accuracy, poor generalization, and high deployment cost in traditional algorithms for mine microseismic waveform classification, a multi-scale residual convolutional neural network (MSRCNN) was designed for the complex time-frequency structures and significant scale variations of microseismic waveforms. Multi-scale convolutions were used to capture local details and global trends of microseismic waveforms. Residual connections were introduced to enhance deep feature transmission and gradient stability. The MSRCNN was conmbined with a bidirectional long short-term memory network (BiLSTM) and a self-attention mechanism to construct the MSRCNN-BiLSTM-Attention model. A weighted loss function combining cross-entropy loss and supervised contrastive learning was used for training optimization. The model was validated using data from multiple mine microseismic monitoring projects. The results show that the constructed model performs significantly better than traditional image recognition models in terms of accuracy, recall, and precision. High accuracy is maintained in transfer tests across different mines. Good robustness and generalization ability are also shown. The MSRCNN module is the core source of model performance. When used alone, it achieves recognition accuracy close to that of the complete model, and it also has fewer parameters. Therefore, it is suitable for deployment in real-time mine monitoring systems with limited computing resources. The complete model has more parameters, but it achieves the best recognition performance. It is more suitable for high-risk scenarios, such as deep high-stress areas, rockburst-prone areas, and areas with frequent microseismic triggering. More reliable recognition ability is provides for microseismic early warning.

    Explosion and flame evolution characteristics of methane-acetylene premixed gas under constant volume conditions
    Liang Yuntao, Bai Jieqi, Wang Lin, Tian Fuchao, Su Weiwei, Zhao Pengtao
    2026, 36(8):  65-73.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0988
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    To investigate the evaluation basis for the explosion risk of mixed gas during the cracking of methane (CH4) to acetylene (C2H2), a 20-L spherical explosion pressure test system was utilized to determine the explosion limits of the mixed gas under low-proportion blending conditions and revise the empirical formula. Simultaneously, Fluent software was employed to simulate the explosion characteristics of the mixed gas in a spherical pressure vessel, and numerical simulations were carried out on three stoichiometric mixtures with acetylene blending ratios of 5%, 50%, and 95%. The results show that the conclusions from experimental tests and numerical simulations are in good agreement. In Stages I and III, the reaction characteristics of the mixed gas are similar to those of single-component combustible gases; in Stage II, the reactions between methane and acetylene are mutually coupled and more stable. The gas density distribution in the container is consistent with the trend of temperature cloud diagrams, showing internal and external density differences bounded by the flame front. The temperature change time at the same monitoring point advances with the increase of the blending ratio, and the density at each monitoring point shows differential changes.

    A decision model for in-field lane-changing at highway traffic risk in combination of flat and longitudinal sections
    Hu Liwei, Ma Siyue, Chen Jiale, Pan Jiangxiong, Yang Can, Gong Qi
    2026, 36(8):  74-83.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0657
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    In order to characterize the driving risks faced by vehicles during lane changing in the combination of longitudinal and flat road sections, and to ensure safe lane-changing maneuvers, this study firstly establishes a unified field model of driving risks and defines the influence factors of road conditions Ri(gradient and curvature of the round curve) to characterize the amplification of the dynamic risk field of the vehicle by such road conditions. Secondly, the influence factors of road conditions are introduced into the model of minimum safe lane change distance, and the level of road safety ξ is proposed to reflect the different influences of different combinations of longitudinal and flat road sections on the minimum safe lane change distance. Finally, a Minimum Safe-Distance Lane-Change (MLC) model for combined road sections was established. The minimum safe lane-changing distance required by vehicles at different speeds, different accelerations, and different combinations of longitudinal and flat lines is validated through real-vehicle experiments. The results show that the speed and acceleration of the vehicle synergistically affect the minimum safe lane-changing distance; the larger the influence factor of the road conditions is, the longer the minimum safe lane-changing distance is; the correlation coefficient between the simulation data and the real-vehicle test data reaches 0.996, which means that the model has high accuracy. Compared with the ellipsoid-based lane-changing model, the F1 score of the lane-changing model proposed in this study reaches 89.45% and 90.99%, which are 7.49% and 8.10% higher, respectively. Compared with the safety potential field lane-changing model, it improved by 4.57% and 4.32%.

    Deformation characteristics of coal samples during entire process of gas migration under triaxial stress conditions
    Kong Xiangguo, Yun Zehao, Lin Haifei, Ji Pengfei, He Di, Yang Songrui
    2026, 36(8):  84-94.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1534
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    To reveal the coupling relationship between gas migration and coal deformation during the development of coal-gas dynamic disasters, full-process deformation tests covering gas adsorption, desorption and seepage were carried out on various coal samples. The correlation between the deformation characteristics of coal mass throughout gas migration and triaxial stress conditions was systematically analyzed, and the effects of gas pressure and axial stress on coal deformation were clarified. The results show that the temporal evolution characteristics of gas adsorption-induced deformation and gas seepage-induced deformation are highly consistent and both present a trend of rapid growth in the initial stage, slow growth in the middle stage and relative stabilization in the final stage. Gas desorption-induced deformation of coal mass follows a trend of rapid decay in the initial stage, slow decay in the middle stage and relative stabilization in the final stage. Gas pressure promotes the whole process of coal deformation induced by gas adsorption-desorption-seepage, while axial stress exerts an inhibitory effect. The influence of gas pressure on coal deformation is more significant than that of axial stress. For coal masses with poor pore connectivity, the time for deformation to reach relative stabilization is significantly prolonged, whereas coal masses with favorable pore connectivity are more sensitive to gas pressure and axial stress.

    Numerical study on dynamic thermal response and failure modes of storage tanks in chemical industrial parks
    Chen Chao, Xiao Shenbin, Wei Lijun, Zeng Tao, Mo Li
    2026, 36(8):  95-101.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0925
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    To reveal the failure mechanism of storage tanks under strong thermal loads in chemical industrial park fires, thereby providing a basis for disaster prevention and safety design, firstly, a double-layer cylindrical flame model was adopted to simulate the thermodynamic behavior of the tank surface. Secondly, combined with the artificial damping method (ADM) and the yield strength and temperature relationship in the European Convention for Constructional Steelwork (ECCS), numerical analysis was conducted on the dynamic response and failure mode of the tank during a fire. Through numerical solution, the distribution of the temperature field and stress-strain field of the tank was obtained. Finally, the failure mode of the tank was determined based on the failure time, and the influence of the tank spacing, liquid level height inside the tank, and pool flame height on the failure mode of the tank was compared. The relationship between different influencing factors and failure time was quantified using the dimensionless analysis method to reveal the thermal response and failure patterns of the storage tank in the fire scenario. The results show that the failure of the tank includes buckling and yielding failure. When the tank spacing is between 12 and 21 meters, the tank undergoes buckling failure. At liquid levels of 3.56 to 8.90 meters, the tank is prone to buckling failure. When the pool fire height is between 6 and 18 meters, the tank undergoes buckling failure. When the pool flame height is 21 meters, the tank undergoes yielding failure.

    Intelligent safety management and risk prevention-control mechanism based on Five-Flow Synergy Theory
    Xu Bo, Zhang Jianwen, Han Yi, Ji Yanming, Ma Shihai
    2026, 36(8):  102-107.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0738
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    To fill the theoretical deficiency of emphasizing practical operation while ignoring underlying mechanisms in existing intelligent safety research and improve the fundamental theoretical system of intelligent safety, proposes an intelligent safety management and risk prevention and control mechanism based on Five-Flow Synergy Theory was proposed. By clarifying the internal collaborative logic of material flow, energy flow, digital flow, information flow and control flow, this study revealed the accident evolution mechanism induced by single flow abnormality and multi-flow coupling imbalance. Targeted prevention and control mechanisms adapted to the five-flow accident causation characteristics were proposed. A four-layer technical architecture consisting of the perception layer, network layer, platform layer and execution layer, as well as a cloud-edge-terminal three-level collaborative architecture were established. Furthermore, a five-flow collaborative management mechanism featuring data penetration, responsibility traceability and cross-domain linkage was constructed, forming a full-chain active risk prevention and control system with full-domain perception, dynamic assessment, intelligent early warning and collaborative control. The effectiveness of the proposed model was verified through typical industrial scenarios including chemical production and emergency rescue. The research results indicate that, the safety management model constructed based on the Five-Flow Synergy Theory can reasonably explain the causes of new-type industrial safety accidents, support full-domain risk perception, intelligent analysis and judgment as well as closed-loop disposal, and effectively enhance safety resilience and prevention and control capabilities.

    Comprehensive monitoring model for safe operation of high-speed maglev trains
    Hu Qizhou, Lei Aiguo, Wang Chen
    2026, 36(8):  108-113.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0310
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    To realize real-time monitoring and risk assessment of the safe operation status of high-speed maglev trains in complex environments, this study adopted a data-driven approach based on multi-source heterogeneous operational data and integrated FMEA and PRA to establish a safety operation monitoring model for high-speed maglev trains. Key physical parameters, including levitation gap, traction current, train attitude, and track conditions, were extracted and analyzed to achieve comprehensive safety evaluation and abnormal behavior identification. Furthermore, a combined subjective-objective monitoring method was proposed, together with procedures for determining monitoring indicator weights and defining hazard levels. The proposed model was validated through monitoring experiments on three high-speed maglev trains. The results show that the comprehensive monitoring values of the three trains are 0.988 4, 0.961 5, and 0.984 8, respectively, all falling within the Level V (slightly dangerous) interval, which indicates the overall safe operation state of the trains. Significant differences are observed in the criticality and risk values corresponding to different monitoring indicators, among which levitation gap and traction current exhibit relatively greater impacts on operational risk. In addition, the monitoring system is capable of performing train operation status monitoring, fault tracking, log alarming, and meteorological warning, while effectively reflecting changes in operational risk.

    Research on acid-activated metakaolin-urea-formaldehyde resin composite cementitious sealing materials
    Dou Guolan, Wang Jingyu, Huo Shuai, Chen Peng, Zhong Xiaoxing, Tang Furong
    2026, 36(8):  114-123.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1440
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    To address coal spontaneous combustion disasters caused by air leakage in underground coal mines, an acid-activated metakaolin-urea-formaldehyde resin composite cementitious material, GM-3 was proposed. The material was developed to overcome the shrinkage cracking and alkaline corrosion of traditional alkali-activated geopolymer sealing materials, as well as the dependence of phosphate-activated systems on heat curing. By incorporating nano-Al2O3-modified melamine-urea-formaldehyde foaming resin (AMUF), the material solidified at room-temperature. The mechanical properties, thermal stability, flame retardancy, and sealing performance were investigated. The results show that GM-3 can be cured at room temperature when the AMUF content is 3%. The 7 and 14-day compressive strengths reach 8.77 MPa and 14.05 MPa, respectively. These values are significantly higher than those of phosphate-activated metakaolin-based geopolymer(MKG). Thermogravimetric(TG) analysis shows that GM-3 has a higher char yield and better thermal stability. Flame retardancy tests show that the peak heat release rate(PHRR) and total smoke release(TSR) are greatly reduced. GM-3 cannot be ignited, indicating excellent fire safety. In simulated air leakage sealing tests, the average sealing efficiency of GM-3 exceeds 90% under airflow rates of 1-6 L/min and pressures of 0.05-0.2 MPa. Its performance is better than that of the comparative lignin-modified alkali-activated coal-based solid-waste geopolymer cementitious sealing material (LFGS).

    Identification and analysis of fatal accidents of training aircraft under uneven distribution of accident types
    Zhang Qingfeng, Cao Yufei, Zhou Yue, Fu Chuanyun
    2026, 36(8):  124-132.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0848
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    To clarify the lethality mechanisms of trainer aircraft accidents with significantly imbalanced accident data, the influencing factors of their lethality were analyzed. The accident data for general aviation trainer aircraft from the National Transportation Safety Board (NTSB) were utilized. Six dimensions of variables, namely time, flight, pilot, aircraft operation, meteorology, and airport, were extracted. Given that fatal accidents accounted for 9.66% of the data, a sample imbalance existed. Resampling techniques, including the Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) sampling, were employed to address this issue. Multiple interpretable machine learning models were subsequently trained to analyze accident causes and fatal accident influencing factors. The results indicate that the LightGBM model combined with Borderline-SMOTE (synthetic sample ratio 0.12) achieves the optimal performance in predicting accident fatality. Its accuracy is 96.04%, and its macro-F1 score is 88.19%. Among the characteristic influencing factors, accidents occurring in the flight-maneuvering phase, the flight-takeoff phase, and in-flight loss-of-control events are positively correlated with fatal accidents. The flight-landing phase is negatively correlated with fatal accidents. Pilots aged over 65 produces a mixed effect. This study, through interpretable machine learning models, reveals the key influencing factors for the fatality of trainer aircraft accidents. It provides an empirical basis for understanding the accident mechanism.

    Establishment and verification of mechanical model for internal pressure strength of bimetallic composite pipes
    Gu Tianping, Han Yuhang, Wang Xin, Wu Ze, Weng Guangyuan
    2026, 36(8):  133-141.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1935
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    To reduce the risk of major safety accidents caused by strength design deficiencies of bimetallic composite pipes in corrosive environments, an X65-316L composite pipe was selected as the research object. A theoretical analytical model and a finite element mechanical model were established based on the plane strain assumption. The mechanical behaviors of single base pipe, equivalent pipe, and composite pipe under different internal pressures (5-30 MPa) were comparatively analyzed. The results show that the bimetallic composite pipe exhibits significantly superior structural performance compared with the base pipe, and its ultimate load-bearing capacity is effectively enhanced. The load is primarily borne by the high-strength base pipe, with the peak von Mises stress located at the base-liner interface, and the liner mainly undertakes the anti-corrosion function. In addition, the equivalent pipe model demonstrates good engineering applicability within the elastic range. Good agreement is achieved between the theoretical predictions and finite element results. The relative errors between the theoretical and finite element results are less than 0.8% for the base pipe model and 14% for the composite pipe model.

    Research on effects of intelligent dispatching mode and ranking pressure on mental workload of mining truck drivers
    Rao Binjian, Jiang Song, Zhang Yalan, Lu Caiwu, Gu Qinghua
    2026, 36(8):  142-149.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1470
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    To investigate the effects of dispatching mode and ranking pressure on the mental workload of mining truck drivers, 24 drivers participated in driving simulation experiments with EEG monitoring. EEG power spectrum integrals, subjective workload scores, and driving performance indicators were analyzed under different task conditions. The results showed that switching from the "binding mode" to the "dispatch mode", particularly with ranking pressure, significantly increased drivers' cognitive demand and effort. The "binding mode" resulted in higher average speeds and fewer braking events, whereas the "dispatch mode" provided faster responses and greater flexibility in unexpected tasks. EEG results indicated that the "dispatch mode" alone maintained mental workload at a moderate level, while its combination with ranking pressure produced a significant synergistic effect, leading to mental workload overload.

    Experimental study on combustion of deposited low-density polyethylene dust induced by ethylene explosion
    Long Huimin, Lyu Pengfei, Yuan Yujun, Liu Zhendong
    2026, 36(8):  150-159.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1212
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    In order to elucidate the combustion evolution mechanism under the interaction between gas explosions and deposited dust, combustion tests were conducted using an independently developed experimental platform for gas explosion-induced dust combustion. These experiments investigated the ignition of LDPE dust triggered by ethylene explosions, with data collected on pressure, temperature, and flame propagation behavior within the explosion chamber. A systematic analysis was performed to examine the effects of ethylene volume fraction and LDPE deposition mass on the explosion dynamics. The results demonstrate that explosion pressure-time curve exhibits a characteristic trend of initially increasing and then decreasing when ethylene concentration ranges from 3% to 7% and LDPE mass is 0, 0.5, 1, 1.5, 2, or 2.5 g. At the same LDPE dust mass, the time required to reach the peak pressure gradually decreases as the ethylene concentration increases. At the same ethylene concentration, the peak pressure of explosion remains relatively stable with increasing LDPE mass, whereas the peak explosion pressure increases significantly with increasing ethylene concentration. At different ethylene concentrations and LDPE dust masses, the temperature-time curves consistently exhibit an initial increase followed by a subsequent decrease. At a constant LDPE mass, the time required to reach the peak explosion temperature generally decreases as the ethylene concentration increases. When the ethylene concentration increases from 3% to the range of 4%-7%, the variation pattern of peak temperature with LDPE mass changes from a two-stage pattern of decreasing first and then increasing to a three-stage pattern of decreasing first, then increasing, and finally decreasing. At a constant ethylene concentration, the flame color changes from blue to bright blue and finally to yellow when LDPE mass is 0 g. As LDPE mass increases to 0.5, 1, 1.5, 2, and 2.5 g, the flame color transitions from blue to bright blue, then to bright yellow, and ultimately to dark red. At a constant ethylene concentration, the flame area, flame continuity, and combustion duration initially increase and then decrease as the LDPE mass increases.

    Study on noise and atomization dust suppression performance of a low-noise supersonic atomization device
    Tao Shuang, Zhang Tian, Tong Linquan, Ge Shaocheng, Li Sheng, Wang Changyou
    2026, 36(8):  160-168.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1307
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    To reduce the high-frequency noise generated by supersonic atomization dust suppression technology, a low-noise supersonic atomization dust suppression technology equipped with foam metal nozzles was developed. Multi-physical field simulations were conducted on the sound field of nozzles. Combined with experiments, this study explored the influences of foam metal material parameters and aerodynamic pressure on spray noise, droplet particle size and dust removal efficiency, analyzed the noise reduction mechanism of porous foam metals in the noise generation process of transonic flow, and selected preferable materials for noise-reducing nozzles. The results show that among various materials, porous aluminum foam delivers superior noise reduction performance, cutting noise by approximately 16.3% in the sound radiation direction to below 60 dB, which is far below national standards. For the same material, the sound pressure level in medium and high frequency bands at sound sources and sound radiation positions rises with the increase of average equivalent pore diameter. Rising pressure also leads to increased sound pressure levels across all frequency bands of different nozzles. Under identical working conditions, the droplet particle size of all nozzles is around 11 μm at the 50% quantity distribution, and the total dust removal efficiency exceeds 84% within 3 minutes of dust suppression. The intricate pore structure inside foam metal Laval nozzles effectively absorbs vibration conduction energy generated during pneumatic atomization, markedly lowering the sound pressure level of sound sources propagating radially through nozzle side walls.

    Research on impact of dynamic response of railway container liquids on vehicle safety
    Chai Guowei, Zhu Dapeng
    2026, 36(8):  169-178.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1376
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    To investigate the effects of longitudinal coupling impact loads on the dynamic performance and structural safety of railway containers, a bidirectional fluid-structure interaction co-simulation model was established by integrating Fluent and Simpack. A 20 ft international standard container with a filling ratio of 70% was taken as the research object. The dynamic response characteristics of the railway container vehicle under a longitudinal coupling impact load of 5 km/h were comparatively analyzed under conditions without baffles and with different numbers of baffles (1 to 3). The influence of liquid sloshing on the structural stress and deformation of the container as well as on the vehicle dynamic performance was systematically examined by the study. The results show that under a 5 km/h longitudinal coupling impact, the peak longitudinal sloshing force and pitch sloshing moment inside the container reach 146.78 and -347.6 kN·m, respectively. Compared with transporting an equivalent mass of rigid cargo, the peak vehicle longitudinal acceleration is reduced from 2.45g to 2.05g. The vehicle pitch angle is significantly increased by transporting liquid cargo, and the peak derailment coefficient of the leading wheelset rises to 0.37. Under the no-baffle condition, significant stress concentration and structural deformation on the front wall of the container are caused by liquid sloshing. After adding three baffles, the peak longitudinal sloshing force is reduced by 36.9%, the peak pitch sloshing moment by 66%, the maximum equivalent stress on the container front wall by 22.9%, and the maximum deformation by 18.6%. Meanwhile, the peak derailment coefficient of the leading wheelset decreases by 64%, and the vehicle pitch angle is notably reduced.

    Research on predicting lower explosion limits of hydrogen-based fuels using machine learning
    Zhang Zhichao, Li Yanchao, Zhang Kai
    2026, 36(8):  179-186.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1140
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    To ensure the safety of hydrogen-based fuels during production, storage, transportation and usage, the LEL test data of H2, NH3 and CH3OH under different initial temperatures and pressures were collected, and a machine learning database consisting of 498 samples was constructed. Based on this dataset, feature correlation analysis was conducted, and the data was split into training and testing sets in an 8∶2 ratio. A five-fold cross-validation strategy was employed to enhance the stability and reliability of neural network model training. Multivariate linear regression models and deep neural network models were established separately. By comparing different activation functions, optimization algorithms, and numbers of neurons, the hyperparameters and network architecture of the neural network were determined. Concurrently, combustion reaction kinetics calculations were performed using a one-dimensional laminar premixed flame model and a detailed reaction mechanism, and the three methods were systematically compared and validated for accuracy. The results indicate that the optimal structure of the neural network is 5-10-12-1. The prediction performance is the best when using Rectified Linear Unit(ReLU) activation function and the adaptive moment estimation algorithm as the optimizer. The coefficient of determination R2 of the model is 0.983 6, the root mean square error (RMSE) is 0.248 6, the mean absolute error (MAE) is 0.201 2, and the maximum MAE compared with the experimental values is 0.25%. The combustion reaction kinetics method has the second-best accuracy, with the maximum MAE of the experimental values being 0.58%. The multiple linear regression model has the lowest accuracy and poor generalization ability. R2 of the model is 0.911 9, the RMSE is 0.296 5, and the MAE is 0.225 1, and the maximum MAE of the experimental values reaches 1.89%.

    Risk analysis and emergency response methods for salt cavern hydrogen storage based on a domain-specific large language model
    Liu Xing, Zhang Wei, Li Ankang, Ji Hu, Sun Changjian, Ye Xiao
    2026, 36(8):  187-196.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0251
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    To accurately analyze potential risks in salt cavern hydrogen storage projects and generate scientifically sound and effective response plans, a risk analysis and emergency response method integrating a composite risk index, RAG, and LoRA fine-tuning is proposed. First, a hierarchical monitoring indicator system is established to calculate the composite risk index and determine the corresponding early-warning level. A multimodal knowledge retrieval database incorporating specialized knowledge from geological reports, academic literature, historical cases, and other sources is then constructed using RAG. Subsequently, the base large language model is fine-tuned using LoRA. Finally, emergency response plans are generated based on the identified risk status, retrieved evidence, and structured prompts. Experimental results show that, on an independent test set, the proposed method achieves F1 scores of 0.91, 0.93, and 0.84 in three core tasks: risk analysis, strategy generation, and scenario generalization, respectively. Ablation experiments show that the RAG-LoRA integrated model achieves an F1 score of 0.88, representing an absolute improvement of 0.16 over the base model. System latency tests indicate that the average time required to generate an emergency response plan is less than 5 s, satisfying the real-time requirements of engineering emergency response.

    Public Safety and Emergency Management
    Dynamic allocation of limited emergency protective materials under constraint of medical treatment capacity
    Zhou Lin, Li Juan, Luo Jing, Wang Xiangyu
    2026, 36(8):  197-205.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1324
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    To address the severe challenges to epidemic prevention and control caused by limited medical treatment capacity and insufficient emergency protective materials at the early stage of major sudden epidemics, research on the dynamic allocation of emergency protective materials was conducted in this study. Firstly, considering the time-varying characteristics of the severity of symptoms among infected patients during epidemic evolution, a Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) epidemic evolution model integrating protective material allocation and optimized patient treatment was proposed to simulate epidemic spread. Subsequently, a multi-period dynamic allocation decision model for limited emergency protective materials was established under constrained medical treatment capacity, and a simulated annealing(SA) algorithm was designed to solve the model according to the problem characteristics. Finally, numerical examples were adopted to verify the feasibility and effectiveness of the proposed method, and sensitivity analyses of key parameters were performed. The results demonstrate that the constructed dynamic allocation decision model can effectively improve the utilization efficiency of limited emergency medical resources, and the dynamic allocation strategy delivers the optimal epidemic prevention and control effect when applied at the early stage of an outbreak. In addition, both the supplementation in emergency protective materials and the expansion of hospital treatment capacity exhibit diminishing marginal effects.

    Evaluation and spatio-temporal characteristics analysis of waterlogging resilience of urban road traffic system
    Yang Jinshun, Gao Xiaohan, Wang Zhaoqiang, Zhang Fahui, Guo Chuanwei, Luan Siliang
    2026, 36(8):  206-215.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0926
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    In order to cope with the interference of waterlogging events on the operation of the urban road traffic system and enhance the adaptability and recovery ability of the traffic system, a road traffic waterlogging resilience model was constructed to analyze the temporal and spatial evolution characteristics of resilience. Based on a geographic information system (GIS), a two-layer analysis platform of road network waterlogging, including a lower-layer rainstorm flood analysis model and an upper-layer two-dimensional hydrodynamic model, was built. The dynamic weight function was introduced to construct the dynamic evaluation model of road network topology, and the travel time ratio was used as the traffic performance evaluation index. Then, the comprehensive evaluation system for road traffic resilience under waterlogging scenarios was constructed. The kernel density estimation method was used to analyze the temporal and spatial dynamic evolution of resilience, and the spatial correlation analysis of resilience was carried out based on the network global Moran's index and the spatial analysis technology of cold and hot spots, so as to reveal the temporal and spatial characteristics of road traffic resilience under waterlogging scenarios. A case study on the resilience of the road traffic system under different rainfall intensities was conducted using the road network in Qingdao Economic Development Zone as an example. The results show that as rainfall intensity increases across scenarios with return periods ranging from 10 to 100 years, the range, depth and duration of road waterlogging show an increasing trend. The peak time of water accumulation advances from 105 min to 90 min, and the proportion of inundated roads increases from 4.75% to 20.08%. The road network resilience shows a decreasing trend, and the minimum value decreases from 0.86 to 0.74. The recovery time shows an extended trend, with the recovery delay increasing by up to 67%. The hot spot (high value) and cold spot (low value) of waterlogging resilience exhibit a clustered spatial distribution. The spatial overlap between cold spots and waterlogged areas reaches 80%, and the hot spot areas shift spatially as the waterlogged area expands. The lower the technical grade of a road is, the greater the impact of waterlogging is, and the failure of local roads leads to contiguous vulnerable areas. The high-density road network has good functional stability under low-intensity rainfall, and the failure of key road sections in sparse road networks causes spatial spillover effects.

    Risk evolution and dynamic assessment of tobacco storage fire
    Ke Wei, Wang Yong, Luo Jun, Li Xin, Yang Angbin, Tong Ruipeng
    2026, 36(8):  216-224.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0055
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    To address the complexity of fire causation, the difficulty of quantifying management and human factors, and the time-dependent accumulation of risk in tobacco leaf warehousing systems, a dynamic fire risk assessment method integrating fuzzy set theory and DBN was proposed. Causation pathways and prevention barrier structures were identified based on the Bow-tie model. fuzzy set theory was introduced to map expert fuzzy judgments into DBN prior probabilities, thereby addressing the data scarcity for critical basic events. A time-dependent DBN model was then constructed to characterize the dynamic evolution of fire risk over successive management cycles. Backward diagnosis and Risk Reduction Worth (RRW) analysis were further combined to trace key hazards and prioritize mitigation actions. The results showed that, under static management conditions, the probability of the top fire event increased from 16.76% to 64.75% over 10 management cycles, while the probability of catastrophic consequences increased from 0.011% to 9.27%, exhibiting a pronounced nonlinear growth pattern. Through backward diagnosis, electrical line fault was identified as the primary physical cause with a posterior probability of 57.25%, whereas neglect of duty by fire safety personnel (49.92%) and non-compliance in hazard inspection and rectification procedures (41.38%) were determined as the dominant management-related causes. RRW analysis further revealed that the risk reduction sensitivity of management and human factors was significantly higher than that of physical factors, with neglect of duty by fire safety personnel (RRW= 1.351 2) and non-compliance in hazard inspection procedures (RRW=1.248 7) ranking first and second, respectively, indicating that strengthening personnel accountability and closed-loop hazard management yields greater risk reduction efficiency than technical upgrades alone. Physical factors provide the necessary conditions for fire occurrence; however, the cumulative effect of management deficiencies is the root cause driving the nonlinear escalation of system risk and the failure of multiple defense barriers. Dynamic risk assessment should incorporate management and human factors as core monitoring dimensions to support the formulation and implementation of differentiated risk control strategies within the dual prevention mechanism.

    Risk-aware probabilistic trajectory prediction method for industrial warehouse safety
    Li Xin, Song Xuanyu, Wang Leyao, Wei Zongshuai, Ke Wei, Tong Ruipeng
    2026, 36(8):  225-232.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0054
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    To improve trajectory prediction accuracy, collision risk identification, and probabilistic assessment reliability in industrial warehouse scenarios, a RAPTP method is proposed. RAPTP employs an SE(2)-equivariant state-space encoder to model multi-agent interactions, combines flow matching with a neural hazard field to jointly generate future trajectories and estimate collision risk, and integrates distributionally robust optimization (DRO)with Mondrian conformal prediction for probability calibration. An IWS benchmark is also constructed, followed by comparative, ablation, and zero-shot transfer experiments. RAPTP achieves a minimum Final Displacement Error(minFDE) of 1.50 m on nuScenes, 16.2% lower than EqMotion. On the IWS benchmark, the near-miss detection Area Under the receiver operating characteristic Curve(AUC) is 34.4% higher than Time-to-Collision(TTC) baseline, the early warning time increases from 1.5 s to 3.8 s, and the prediction interval coverage reaches 94.2% at a target coverage level of 90%. In occlusion scenarios of the Hubei dataset, the detection AUC reaches 89.2%. These results demonstrate that RAPTP achieves effective trajectory prediction, collision warning, probability calibration, and cross-scenario transferability.

    Linear-beam smoke-methane composite detection algorithm based on dual-wavelength features
    Li Boning, Wang Li, Zhang Xi, Lyu Xunzhai, Shi Yisong
    2026, 36(8):  233-242.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1432
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    To address the problems of high cost, asynchronous signals, and high false and missed alarm rates in complex scenarios of traditional single detection systems when fire smoke and methane leakage coexist in long-distance spaces, a linear-beam smoke-methane composite detection algorithm based on dual-wavelength characteristics was proposed. A long optical path spectral measurement platform with a total length of 120 m was constructed, the spectral characteristics of smoke attenuation and methane absorption were systematically analyzed, and a dual-wavelength detection architecture at 850 nm and 1 653.7 nm was designed. The algorithm adopted a three-branch parallel structure: the smoke detection branch learned the temporal attenuation characteristics of dual-wavelength signals through dual input channels. The methane detection branch extracted the morphological features of absorption peaks combined with an attention mechanism, and then captured dynamic signal changes via a GRU. The merging branch realized comprehensive judgment of three types of scenarios through feature fusion and temporal accumulation logic. Comparative experiments with six traditional fire and gas detection algorithms show that the proposed algorithm achieves an accuracy of 95.0%, false alarm rate of 2.3%, missed alarm rate of 1.7%, response time of 6.9 s and inference time of 0.28 s, with overall performance significantly superior to traditional methods.

    Disaster Prevention and Mitigation Technology and Engineering
    Geological disaster risk assessment based on SCE
    Yu Zhonghai, Wang Lu, Liu Qian, Yan Libo, Fang Qiang, Duan Longmei
    2026, 36(8):  243-250.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1447
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    To accurately identify the distribution of geological disaster risk in the 2 630 km2 hilly and mountainous areas of southern Jinan City and to support refined regional disaster prevention and mitigation, a multi-source-data-integrated assessment method at the village and community was proposed. First, surface deformation field was obtained from L-band Synthetic Aperture Radar (SAR) data acquired by the domestic Lutan-1 satellite using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. Then an assessment index system of “hazard source-exposed elements” was constructed by integrating multi-source information, including geological hazards, socio-economic data, topography, and meteorological factors. Finally, the level difference maximization combination method was employed to integrate the Analytic Hierarchy Process (AHP) and the Entropy Weight Method(EWM) for determining subjective and objective combination weights. Subsequently, a risk assessment model was constructed by introducing SCE multi-attribute decision-making model. The results show that 163 high-risk villages and communities are identified among the 1095 villages and communities in the study area, primarily concentrated in Zhangqiu District and Laiwu District. The maximum surface subsidence rate, the number of geological hazard sites, and the resident population are the three main factors affecting geological disaster risk in the study area. For high-risk villages and communities, priority should be given to strengthen safety-production supervision of open-pit mines, hazard inspection and rectification for self-built houses, hillside restoration, waterlogging prevention and control, and geological disaster monitoring and early warning.

    Construction and parameter analysis of vulnerability measurement model for waterlogging disaster in underground space of Beijing
    Lou Hezhuang, Jiang Hao
    2026, 36(8):  251-259.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0264
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    To investigate the risk of waterlogging hazards in underground spaces, taking 3 980 underground space samples from Beijing's central urban area as the research subject, the Zipf distribution was employed to characterize the non-normal long-tail distribution features of their scale areas. A vulnerability measurement model for underground space waterlogging based on multi-source data fusion was constructed. The dynamic evolution patterns of risks under varying inundation threshold scenarios were quantified, and the influence mechanisms of distance sensitivity (k), population density (p), and usage importance coefficient (c) on vulnerability were elucidated. Differentiated prevention and control measures were proposed for underground spaces with different purposes. The results show that the risk of waterlogging disasters in Beijing's central urban underground spaces exhibits a core-periphery differentiation characteristic. As the inundation height threshold increases from 0.2 m to 0.8 m, the spatial distribution of risks evolves from point-like aggregation to band-like extension. The disaster resistance capacity of low-risk underground spaces diminishes rapidly with increasing inundation height thresholds. The straight-line distance is significantly negatively correlated with waterlogging vulnerability, whereas population density shows a significant positive correlation. With the rise in the usage importance coefficient, the risk associated with multi-functional underground spaces increases markedly. Engineering measures aimed at eliminating surrounding water accumulation points and reducing human activities in these areas during urban flooding can significantly mitigate risks.

    Machine learning-based calibration of PBM meso-parameters and numerical simulation of unloading rockburst
    Yu Hao, Wang Chao, He Ziwang, Liu Yu, Jin Zijun, Qi Shuai
    2026, 36(8):  260-268.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1168
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    To accurately determine the mesoscopic parameters of the PBM, an integrated parameter calibration framework based on machine learning was developed. First, mesoscopic parameter samples were generated using orthogonal design, Latin hypercube sampling (LHS), and stratified sampling, and a macro-mesoscopic parameter mapping database was established through automated batch uniaxial compression simulations in PFC2D. Subsequently, particle swarm optimization (PSO), sparrow search algorithm (SSA), and exponential triangle optimization (ETO) algorithms were employed to optimize the hyperparameters of random forest (RF), back propagation(BP) neural Network, support vector regression (SVR), and K-nearest neighbor (KNN) models, and a total of 16 machine learning models were constructed for performance comparison. Finally, the optimal model was applied to calibrate the meso-parameters of marble from the Jinping II Hydropower Station, followed by numerical simulations of unloading rockburst. The results indicate that the ETO-BP model achieves superior predictive performance compared with the other models. The calibration errors of the uniaxial compressive strength, elastic modulus, and Poisson's ratio for five rock specimens are all less than 5%. Under unloading conditions, the simulated rockburst process evolves through four stages, namely the quiet stage, small-particle ejection stage, slab spalling accompanied by particle ejection stage, and overall collapse stage, exhibiting typical failure characteristics including particle ejection, slab splitting, and block collapse.

    Occupational Health
    Participatory ergonomics intervention study involving WMSDs for rail vehicle assembly personnel
    Jin Xu, Cao Lei, Dong Yidan, Jin Xianning, Wang Shijuan, He Lihua
    2026, 36(8):  269-275.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0399
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    To effectively prevent WMSDs among relevant occupational populations in China, this study evaluated the effects of a participatory ergonomic(PE) intervention on WMSD symptoms, ergonomic workload exposure, and WMSD-related knowledge among rail vehicle assembly workers. A 12-month intervention study was conducted among 181 workers in an assembly workshop of a rail vehicle manufacturing enterprise. Ladder/stool optimization and ergonomics training were implemented. The Chinese Musculoskeletal Disorders Questionnaire and Quick Exposure Check (QEC) were used to assess WMSDs prevalence, ergonomic load, and awareness at baseline, mid-term follow-up (approximately 6 months after intervention), and final follow-up (approximately 12 months after intervention). GEE was used to analyze the longitudinal data. The results show that, at the final follow-up, the marginal prevalence of WMSDs decreases from 52.77% at baseline to 33.44%, with an odds ratio (OR) of 0.63, which is statistically significant (P=0.002), indicating a 37% risk reduction. The QEC scores for the neck, shoulder, back and wrist decrease significantly with regression coefficients (β) of -1.61, -9.20, -3.20, and -5.98, respectively (all P<0.01), whereas work pace and psychological stress do not change significantly. The WMSDs awareness score increases from 1.35 to 3.80 (P<0.001). Participatory ergonomics intervention is effective in reducing WMSDs risk among vehicle assembly workers, and long-term intervention (≥12 months) has a significant effect on improving ergonomic load in key body regions.

    Intelligent Safety Technology
    A multi-agent differential game analysis of digital intelligent technology enabling gas safety governance: an industry-university-research perspective
    Zhang Hua, Zhang Zhihui, Cao Cejun
    2026, 36(8):  276-286.  doi:10.16265/j.cnki.issn1003-3033.2026.08.1589
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    To improve the governance level of urban gas safety management, a three-player differential game model involving upstream enterprises, downstream enterprises, and research institutions was developed under the empowerment of digital and intelligent technologies in gas safety management. Particular attention was paid to the scenario in which the upstream enterprise shared various costs incurred by the downstream enterprise. The equilibrium strategies and profits under four different scenarios were systematically compared, and numerical simulations were conducted to examine the effects of key parameters on the Pareto improvement. The results show that the adoption of digital and intelligent technologies by downstream enterprises not only enhances the level of gas safety but also increases the profits of all participants. Moreover, the implementation of a dual cost-sharing mechanism by the upstream enterprise yields superior Pareto improvements for all members. In addition, demand price elasticity, gas safety management efficiency, and its associated costs significantly affect the upstream and downstream enterprises under the Pareto improvement induced by the dual cost-sharing mechanism, whereas the contribution of digital and intelligent technologies and their investment costs mainly influence the profits of research institutions and the magnitude of the Pareto improvement.

    Applicability of artificial intelligence-assisted decision-making for inherent safety in chemical process
    Xu Honglong, Hao Jinqing
    2026, 36(8):  287-293.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0898
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    To investigate the feasibility of applying artificial intelligence (AI)to inherent safety decision-making for chemical process, a decision-making framework comprising data collection, risk identification, and strategy development was developed to address delayed risk identification, the challenges of dynamically determining control parameters, and the lack of decision support under complex operating conditions. Deep learning, knowledge graph, and reinforcement learning were integrated into the safety design process. The framework was evaluated using representative process scenarios, including exothermic reaction systems, storage and transport of easily combustible light gases, high-pressure gas compression, acid-base neutralization reactions, and oxygen-containing organic reactions. The model is validated in terms of risk identification, path optimization, and strategy development using results from simulations and case studies. Results indicate that the model can dynamically identify the high-risk process pathways and conduct correlation analysis of the parameters. Decision outputs can be produced in the following scenarios: temperature control of the exothermic reactions, identification of the leakage risk zone, abnormal warning for high-pressure systems, pH drift control, and determination of explosion limits. Nevertheless, the model's application is restricted by the following: interpretability, data quality, interface compatibility, and regulatory integration. AI can assist with the integration of multiple data sources, identification of complex risks, and generation of design leads to integrate the multi-source data systems and complex risks. Its use is however determined by the particular process scenario. Engineering implementation will require improved model transparency, data harmonization, and system integration.

    A fatigue assessment for railway maintenance workers based on 3D work posture
    Xia Yuxiang, Song Yubo, Mo Junwen
    2026, 36(8):  294-302.  doi:10.16265/j.cnki.issn1003-3033.2026.08.0135
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    In order to investigate the relationship between work postures and worker fatigue, a fatigue assessment method based on 3D work posture features is proposed. The study examines the association mechanism among posture characteristics, energy expenditure, and fatigue accumulation during working processes. A digital assessment system for working postures is established using 3D human body keypoints to extract movement features. On this basis, by integrating energy expenditure calculation methods with heat transfer theory, a transformation model from energy expenditure to heat dissipation and subsequently to fatigue level is developed, enabling quantitative characterization of worker fatigue. Based on experimental data from a railway maintenance depot, comparative experiments on fatigue assessment methods and correlation analysis between energy expenditure calculations and physiological indicators are conducted. The results show that the proposed method achieves an average accuracy of 90.22%, outperforming other methods, with precision and F1 score also significantly higher. Moreover, the assessment results exhibit a significant correlation with physiological indicators. The findings indicate that a quantifiable relationship exists among posture features, energy expenditure, and fatigue levels. The proposed method enables dynamic characterization of fatigue states during working processes, providing a quantitative basis for fatigue identification and process analysis in railway maintenance operations.