Table of Content

28 March 2025, Volume 35 Issue 3

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Safety social science and safety management Safety engineering technology Public safety Technology and engineering of disaster prevention and mitigation Emergency technology and management Occupational health

Safety social science and safety management

Mechanism of risk perception on safety cognition of construction workers in underground caverns

JIANG Xin, LI Fengbiao, PENG Jiayu, JIAN Li, JIN Lianghai

2025, 35(3):  1-9.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1121

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In order to improve the safety cognition level of underground caverns construction workers, safety requirement and safety capability were introduced as mediating variables, and occupational burnout was used as a moderating variable to construct a moderated chain mediation model. A structured questionnaire was designed using 5 scales: risk perception, safety requirement, safety capability, occupational burnout and safety cognition. A questionnaire survey and data analysis were conducted on 312 underground caverns construction workers, and SPSS 26.0 and AMOS 26.0 software were used to test the mediating effect and moderating effect.The results show that risk perception directly and positively affects the safety cognition of underground cavern construction workers, while occupational burnout plays a negative moderating role between the two. Risk perception also affects the safety cognition of construction workers through the independent mediating effect of safety requirement and safety capability, as well as through the chain mediating effect of safety requirement and safety capability. Therefore, motivating and improving the risk perception, safety requirement and safety capability of construction workers, while reducing their occupational burnout, can effectively improve their safety cognition level.

Safety risk dynamic evaluation of fireworks production enterprises

WANG Feiyue, WANG Xinyu, ZHANG Wenjun, LIU Hui

2025, 35(3):  10-18.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0761

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To reduce the safety risk of fireworks production enterprises and eliminate the subjectivity and static limitations of the current safety evaluation mechanism, an evaluation indicator system of the safety risk for firework production enterprise was constructed based on the grounded theory (GT). The evaluation values and weights of the evaluation indicators were calculated by applying the frequency-based Analytic Network Process-Back Propagation Neuron Network (ANP-BPNN) model. The dynamic evaluation of the safety risk level of firework production enterprise was achieved based on SD, and verified with an enterprise involved in a major fireworks explosion accident of fireworks. The results show that the dynamic evaluation model of safety risk for fireworks production enterprises can accurately capture the development trend of the safety risk. The interval between safety risk evaluations of fireworks production enterprises is flexibly adjusted based on predicted time for safety risk level to reach the higher risk. The continuous dynamic evaluation of safety risk can ensure that the enterprise always maintains a safe production state.

Research on influence of safety motivation on construction workers' safety behavior based on AMO-SDT

YANG Su, YAO Wenbao, WANG Ting, CHENG Baoquan, ZHU Suyuan

2025, 35(3):  19-27.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0931

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In order to examine how construction workers' safety motivation affects their behavior, SDT was used to divide the safety motivation into two types: autonomous and controlled. Subsequently, a hypothetical model linking workers' safety motivation, opportunity, ability, and behavior was constructed by integrating the analytical framework of the AMO theory. Finally, 469 frontline construction workers were taken as the research subjects for data collection, and structural equation modeling (SEM) was used for hypothesis testing and empirical analysis. The results show that the autonomous safety motivation of construction workers is significantly positively correlated with safety behavior, and the controlled safety motivation of construction workers is significantly negatively correlated with safety behavior. Safety motivation and safety competence play a positive moderating role between workers' autonomous safety motivation and safety behavior, while they show a significant negative moderating relationship between workers' controlled safety motivation and safety behavior.

Recognition of personnel fatigue state and unsafe behavior based on computer vision

LI Hua, WU Lizhou, ZHONG Xingrun, GUO Liangwei, CUI Yuxin

2025, 35(3):  28-35.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0749

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Taking improving the safety and efficiency of tower crane operation as an example, a method of integrated identification of fatigue state and unsafe behavior was proposed in order to detect the potential safety hazards of drivers in real time. A live video stream was captured via a camera, and the video was analyzed and pre-processed to extract critical information for identifying subsequent fatigue and unsafe behavior. In terms of fatigue state recognition, the analysis method based on the state of eyes and mouth was used to monitor the physiological indicators such as the state of eyes opening and closing, the blink frequency and yawn frequency. In terms of unsafe behavior identification, computer vision and deep learning technology were combined to detect the potential dangerous operations of drivers in real time, thus ensuring timely detection of safety risks. The results show that the performance of the optimized YOLOv5-ECA(Efficient Channel Attention) model is significantly improved in fatigue state and unsafe behavior recognition. The accuracy rate and recall rate of the model on the test set are more than 90%, showing good recognition ability.

Classification and recognition of unsafe behaviors of tobacco warehouse personnel based on improved YOLOv11

KE Wei, ZHU Quanjie, CHEN Changmao, WU Chengyi, LIU Yan, ZHANG Yanlin

2025, 35(3):  36-44.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1598

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To ensure the safety of personnel and property within the storage environment, the traditional YOLOv11 object detection algorithm was improved, and a method and model to identify unsafe behaviors of personnel in the complex environment of tobacco warehouses were proposed. First, a statistical analysis of common unsafe behavior types in tobacco storage was conducted, and the classification of unsafe behaviors of warehouse personnel was explored, including item-related, action-related, and area-related unsafe behaviors. Second, based on the characteristics of unsafe behaviors of warehouse personnel, a dataset augmentation and denoising preprocessing approach was proposed to enhance fine-grained feature extraction, and introduced to improve the saliency mapping of personnel behaviors. Then, the YOLOv11 algorithm was improved through functional enhancement modules and K-means++ anchor box optimization, and a fast detection method for unsafe behaviors of tobacco warehouse personnel was proposed. Finally, the proposed method's effectiveness was validated by comparing with self-built datasets and the open Microsoft COCO dataset. The results show that the method can quickly and effectively identify unsafe behaviors of warehouse personnel, with a significant improvement in recognition accuracy compared to traditional methods(accuracy rate is 94.91% and 88.69% respectively).

Study on parallel safety & security management and its model

WANG Yuanjie, WANG Bing, SHI Zhiyong, CHEN Jiaxin

2025, 35(3):  45-51.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0625

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In order to enrich the safety & security management theories and innovate the safety & security management paradigm, the research work of parallel safety & security management was carried out based on the parallel system theory and safety & security management theory. Firstly, starting from the parallel system theory and combining with the knowledge of modern safety & security management, the concept of parallel safety & security management was explored and the parallel safety & security management conceptual model was proposed. On this basis, the parallel safety & security management model was constructed and explained. Finally, taking urban safety & security as an example, this paper analyzed the application of the parallel safety & security management model. The research shows that parallel safety & security management is a new paradigm for safety & security management through realizing the virtual-real interaction between artificial safety & security systems and real safety & security systems to achieve multiple objectives such as solving complex safety & security problems, effective implementation of safety & security solutions and efficient training of personnel. Its model has the characteristics of breaking the constraints of real-world conditions, flexibility and scalability, high performance distributed computing, redundancy and fault tolerance, simulation prediction and continuous improvement, task scheduling and load balancing, which can provide a model reference for safety & security management of complex systems, such as urban safety & security management.

Enterprise safety risk management model of machinery manufacturing industry based on improved YOLOv5

ZHANG Hao, HAIMUDULA Aierken

2025, 35(3):  52-59.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0471

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In order to improve the efficiency and accuracy of safety risk management in machinery manufacturing enterprises, the Bayesian network and machine vision technology were combined. Based on improved YOLOv5, Intersection over Union(IoU) values of safety hazard events occurring at the operation site were calculated. By leveraging the audit risk assessment in conjunction with AHP to derive the danger weights, the prior probabilities of the root nodes of Bayesian network were determined. Bayesian network model and design management system were established to realize closed-loop control. A safety risk management model of machinery manufacturing enterprises was constructed and verified by examples. The results show that the model has a more accurate identification and evaluation ability, and can find some potential safety hazards, so as to optimize the current management process. At the same time, the model also successfully realizes the effective combination of qualitative and quantitative analysis, integrates the expert experience and data quantification results, and confirms each other, so that the risk assessment results have a certain improvement in scientificity and reliability, which can provide a practical new idea for safety risk management.

Safety engineering technology

Four-dimensional trajectory point control method for TBO

WANG Yantao, ZHANG Tianwen, SHI Tongyu

2025, 35(3):  60-68.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0649

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To meet the requirements of the future Trajectory-Based Operation (TBO) mode, improve air traffic safety and efficiency, and obtain executable four-dimensional (4D) trajectory results with multi-point control, this study first established a fundamental 4D trajectory dynamics model by integrating atmospheric environment modeling, aircraft point-mass motion modeling, and performance modeling. Subsequently, a 4D trajectory simulation framework was developed using Simulink to validate the executability of the fundamental dynamics model. Following this, an optimal control-based 4D trajectory waypoint control model was constructed with reference trajectories as optimization targets, where the waypoint control problem was transformed into a nonlinear programming problem through the Radau pseudospectral method. Finally, comparative analyses were conducted using historical flight data from QAR. Results demonstrate that the simulated velocity and mass parameters exhibit zero deviation from QAR records, while median errors in longitude, latitude, and altitude are 0.000 11°, 0.001 2°, and 19.24 m, respectively, all satisfying safety separation requirements. Critical parameters including position, time, and heading angle at selected waypoints showed zero deviation. Notably, strict control of 13 waypoints is achieved within a 65-minute flight segment.

Research on tunnel fire detection based on improved YOLOv8s model

WANG Chunyuan, LIU Quanjie

2025, 35(3):  69-76.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1181

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To accurately and efficiently detect fires in complex tunnel environments, an enhanced YOLOv8s-based tunnel fire detection algorithm was proposed. Firstly, the Cross-Stage Partial Transformer Block (CSP-PTB) module was introduced to reconstruct the backbone network structure, thereby reducing computational complexity while preserving feature extraction capabilities. Secondly, CBAM was integrated to enhance the perception of the model of key areas and improve the discriminative power of feature representation. Finally, the Normalized Wasserstein Distance (NWD) loss function was employed to optimize the training process, effectively addressing the issue of insufficient detection accuracy for small targets. Experimental results demonstrate that the improved YOLOv8s model achieves a mean average precision (mAP) of 0.848, representing a 2% improvement over the original YOLOv8s model. The recall rate reachs 0.812, marking a significant increase of 9.3% compared to the original model. Additionally, the computational cost (GFLOPS) of the improved model is reduced by 6.7%, achieving dual objectives of performance enhancement and efficiency optimization. Compared with mainstream object detection models such as Faster R-CNN(Faster Region-based Convolutional Neural Network), SSD(Single Shot MultiBox Detector), and YOLOv5s, the improved model exhibits superior performance, with mAP improvements of 7.3%, 10.1%, and 4.2%, respectively, thus meeting the stringent requirements for tunnel fire detection.

Chemical process risk assessment utilizing SDG and CBR methodologies

CHEN Wentao, YANG Mingkai, WANG Wenhe, SONG Hongwei, SUN Chao

2025, 35(3):  77-84.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1368

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To comprehensively and efficiently analyze and evaluate chemical process risks, a risk analysis and evaluation auxiliary system for chemical processes was designed and developed based on SDG and CBR. The system was integrated with foundational verification functions for layer of protection analysis (LOPA) and safety integrity level (SIL), enabling semi-quantitative risk analysis and evaluation. Additionally, CBR technology was utilized to effectively preserve and reuse historical case experiences in chemical processes, thereby enhancing the efficacy of HAZOP and the auxiliary evaluation system. The system was applied to conduct a risk evaluation of the propylene oxidation to acrylic acid process. The results demonstrate that the system aligns closely with the independent risk evaluation outcomes of the enterprise, exhibiting strong practicality and reliability. The SDG-CBR coupling reasoning mechanism is verified to overcome the reliance of traditional HAZOP on manual experience, achieving a qualitative-to-semi-quantitative transformation in hazard identification while enhancing evaluation efficiency and accuracy.

Radiation noise suppression and dose information extraction method based on FPGA

XU Shoulong, HOU Zhixiong, WEI Cuiyue, ZOU Shuliang

2025, 35(3):  85-91.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1820

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In order to improve and advance the nuclear radiation detection and monitoring integrated technology based on pixel sensors, a radiation noise suppression and nuclear detection method leveraging the parallel advantages of FPGA was proposed, with corresponding programs developed. By analyzing the characteristics of radiation noise signals, radiation noise suppression and two-dimensional wavelet transform programs based on FPGA were developed to output clear radiation field images. The images were decomposed into horizontal, vertical, and diagonal components, and the results of linear fitting statistics for each component were investigated to identify the component with the best linear fit. The research results demonstrate that the FPGA program modules effectively execute radiation noise suppression and nuclear detection functions in images. After noise reduction, the peak signal-to-noise ratio(PSNR) of the images is improved by approximately 11 dB. The diagonal component is shown to best characterize the radiation response information of the images, achieving a linearity of 0.99624 in linear fitting for different dose rates.

Slope stability prediction model based on multi-strategy improved SSA for optimizing KELM

QI Yun, XUE Kailong, LI Xuping, WANG Wei, BAI Chenhao, JI Zhunze

2025, 35(3):  92-98.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0134

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In order to predict the slope state more accurately and effectively prevent the slope instability accident, an improved ISSA-KELM slope stability prediction model was proposed. Firstly, six main factors such as bulk density and cohesion in slope instability characteristics were used as prediction indexes to establish a data set for slope stability evaluation. Secondly, SSA was enhanced by incorporating Sine chaotic mapping, Levy flight strategy, dynamic adaptive weights, and fusion of optimal explosion strategy and reverse learning. These improvements aimed at enhancing the global search capability and stability of SSA. Subsequently, ISSA was employed to optimize the kernel parameter ψ and regularization coefficient C in KELM for improved prediction accuracy while avoiding overfitting issues associated with KELM. The results show that the accuracy rate, precision, recall rate and F₁ score of ISSA-KELM model reached 0.945 9, 1, 0.866 7 and 0.929, respectively, which are superior to SSA-KELM, PSO-KELM and PSO-SVM models, and the predicted results of the model are the closest to the actual values. It shows that the established ISSA-KELM model has strong generalization ability.

Influence of temperature on combustion features of lithium-ion electric bicycles in confined spaces

LI Qingwei, ZHU Yerui, YANG Zhixiang, LYU Ziqi, KANG Furu, REN Lifeng

2025, 35(3):  99-106.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0620

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To address the safety risks caused by rapid fire spread and thermal smoke accumulation in confined spaces such as basements and underground warehouses due to lithium-ion electric bicycle fires, this study combines experimental and numerical simulation methods to investigate the temperature field evolution, flame characteristics, and smoke diffusion behavior of lithium-ion electric bicycle combustion under different ambient temperatures, clarifying the effect of temperature on combustion characteristics. The results show that under experimental conditions, the average temperature at the combustion center in the confined space is approximately 600 ℃, with a peak temperature exceeding 920 ℃. Ambient temperature significantly affects the initial stage of lithium-ion electric bicycle combustion. In a 40 ℃ environment, the time for combustion to enter the rapid development phase is shortened by 20 seconds compared to 20 and 0 ℃ environments. The time required for nearby temperatures to reach the ignition point of the lithium-ion electric bicycle is reduced by approximately 25 seconds compared to 20 and 0 ℃ environments. After about 80 seconds, the temperature rise rates converge. At 20 seconds, the flame morphology of lithium-ion electric bicycle combustion differs noticeably across environments: the flame height at 40 ℃ is approximately 1.15 times that at 20 ℃ and 1.32 times that at 0 ℃. Flame morphology converges after about 80 seconds. Within the first 30 seconds, the smoke diffusion velocity and production rate in a 40 ℃ environment are significantly higher than those at 20 and 0 ℃, but smoke concentrations stabilize to similar levels after approximately 50 seconds.

Research on explosion process of fire extinguishing bombs and scattering characteristics of fire extinguishing agents

LI Cong, HE Zequn, YANG Gaohao, XU Wenbo, WANG Yuepeng

2025, 35(3):  107-114.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0400

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In order to further study the detonating effect of UAV, and improve the fire extinguishing efficiency of fire bombs, ANSYS Workbench software was used to establish a finite element model of fire bombs, and the explosion and dispersion process of fire extinguishing agent was simulated to clarify the impact of different detonation heights on the dispersion characteristics of fire extinguishing agent. Meanwhile, full-scale experiments on the vertical dropping of fire extinguishing bombs by unmanned aerial vehicles at different detonation heights were conducted. The results show that the simulation data such as the explosion process of fire extinguishing bomb, the throwing process of fire extinguishing agent and the spreading radius of fire extinguishing agent are in good agreement with the full-scale test results. After the fire extinguishing bomb explodes, the extinguishing agent disperses in the air in a cone shape and spread evenly in the horizontal direction, and the spreading shape is roughly circular. With the increase of detonation height, the landing time of fire extinguishing agent increases, and the horizontal velocity of fire extinguishing agent decreases. The increase of landing time plays a dominant role relative to the decrease of horizontal velocity, resulting in the increase of the spreading radius of fire extinguishing agent with the increase of detonation height. When the detonation height of the fire extinguishing bomb increases from 5 to 12 m, the numerical simulation data of the fire extinguishing agent spread radius increases from 2.04 to 3.56 m, and the error between the numerical simulation data and the experimental data is within 5%.

Lightweight neural network combined with depth camera for miner target detection and localization

ZHANG Miao, WANG Xiaojun, LEI Jingfa, ZHAO Ruhai, LI Yongling

2025, 35(3):  115-124.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0863

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To prevent miners from mistakenly entering dangerous areas, a lightweight underground miner object detection model based on YOLOv5s-MPD was proposed, which combined with depth camera to locate miner targets and detect whether miners had entered dangerous areas in real time. Specifically, the MobileNetv3 lightweight neural network was used as the backbone feature extraction network to significantly reduce the model size. Secondly, Polarized Self-Attention (PSA) module was introduced to enhance the perception of targets. Finally, Deformable Convolution Network v2 (DCNv2) was used to replace the standard convolution in the C3 module of the feature fusion layer, solving the problem of partial feature information loss in conventional convolution. The improved model was used in combination with the color images obtained by the depth camera to detect miner targets and obtain the spatial three-dimensional coordinates of the target center points. The results show that compared with YOLOv5s, the improved model reduces the number of parameters and computation by 83.54% and 77.03%, respectively. The model size is only 3.4 MB, and a detection speed of 70.2 f/s, which is increased by 54.97%. The mean average precision is 0.825. Compared with mainstream object detection models, the improved model has a more balanced number of parameters, computation, model size, detection speed, and mean average precision. In the actual positioning accuracy test, within a range of 1-8 meters, the average absolute error and average relative error of the distance between the camera and the miner target were 0.11 meters and 1.74%, respectively. The maximum absolute error and maximum relative error were 0.25 meters and 2.96%, respectively. In the dynamic detection, the miner target could be detected and its location information output, with a detection success rate of 97.5%.

Influence of CO₂/N₂ ratio and O₂ volume fraction on low-temperature oxidation characteristics of coal

WU Chunlei, SHI Bobo, XUE Yong, GUO Qingxu

2025, 35(3):  125-132.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1568

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To optimize the mixed inert gas fire suppression technique for goafs, the effects of CO₂/N₂ ratio and O₂ volume fraction on the low-temperature oxidation characteristics of coal were investigated. Taking the long flame coal from Dongxia mine in Gansu as the research subject, a temperature-programmed experimental system combined with gas chromatography analysis was employed to carry out low-temperature oxidation experiments under various CO₂/N₂ ratios (ranging from 0∶10 to 10∶0) and O₂ volume fractions (6%, 10%, 14%, and 18%). The results indicate that at the same O₂ volume fraction, as the CO₂/N₂ ratio increases, the rates of oxygen consumption and CO production of the long-flame coal gradually slow down, with the decline rates increasing as the temperature rises. The apparent activation energy of the long-flame coal increases gradually with the increase in the CO₂/N₂ ratio during the slow oxidation stage, while it initially increases and then decreases during the accelerated and rapid oxidation stages, reaching the maximum value at a CO₂/N₂ ratio of 4∶6.

Risk analysis of homes in landslide-prone areas based on wall crack characteristics

QIN Hanxuan, GUI Lei, ZHANG Wenfang, HAN Youming

2025, 35(3):  133-141.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0503

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To reduce building risks in landslide-prone areas and guide disaster mitigation measures, a comprehensive vulnerability analysis method was proposed based on Entropy Weight Method(EWM)-Analytic Hierarchy Process(AHP) and trapezoidal-semi-trapezoidal membership functions. Using the Sifangbei landslide in Wanzhou District, Chongqing, as a case study. The landslide failure probability was calculated using the Morgenstern-Price and Monte Carlo methods. Field surveys of building attributes were used to analyze landslide hazards and validate the analysis method. The results indicate that the composite model using crack indicators provides reliable vulnerability analysis for buildings in the Sifangbei landslide zone. The proposed method is feasible. The landslide is most dangerous when the reservoir water level drops from 175 m to 145 m. Combined with a 50-year rainfall event, leading to over 7.5 million yuan in economic losses. Vulnerable buildings are mainly located in the middle and rear parts of the landslide and along its right boundary.

Clustering and correlation analysis of factors causing cross operation accidents in construction

LIU Qingfeng, HU Changming, ZHAO Jiangping

2025, 35(3):  142-150.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0717

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In order to effectively prevent cross operation accidents in the construction process, firstly, 130 cross operation accident cases nationwide from 2010 to 2022 were collected as research objects, and an evaluation system for cross operation in construction was established based on the grounded theory. Then, according to the relationship between the indicators, a spectral clustering algorithm was used to analyze the combination of accident causes. The weight of each cause factor was allocated, and different levels of risk were divided by principal component analysis and analytic hierarchy process. Finally, the Apriori algorithm was used to obtain the correlation chain of each factor in the causative combination. The results show that the evaluation system includes 5 core categories, 13 main categories and 40 categories. The core category that has the greatest influence on the risk of cross operation in the construction process is technical management. The 40 categories are grouped into 4 causa l combinations, among which 1 is high risk, 1 is medium risk, and 2 are low risk. In addition, for the complex cluster of factors, the key combination of causes is identified and targeted preventive measures are proposed to reduce the probability of cross-operation accidents.

Text mining of causes of hot working accidents based on 24Model

NIU Maohui, LI Weijun, LIU Yin, WANG Lu

2025, 35(3):  151-158.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0757

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In order to systematically explore the root causes of industrial hot work accidents through a large amount of text data, a text mining method based on 24Model was proposed. Firstly, 220 hot work accident reports were collected and sorted as datasets, and a 24Model classifier based on Bidirectional Encoder Representations from Transformers (BERT) was constructed. The pre-trained model was used to train and evaluate the accident report dataset to construct a classification model. Then, through the combination weight of the Keyword extraction algorithm based on BERT (KeyBERT) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, combined with the 24Model framework, a keyword index system for hot work accident text was established. Finally, the interrelationships between accident causes were obtained through the analysis of the network co-occurrence relationship between text-mining keywords. The results show that the BERT-based 24Model classifier model can systematically and accurately determine the causative categories of hot work accidents. The weight of the safety management system was the largest among the 4-level keyword index systems obtained through the combination of weights. Furthermore, 7 key causative factors of hot work accidents were obtained by combining them with the co-occurrence network analysis. This shows that 24Model can strengthen the interpretability of text mining results, which provides an important reference for the prevention and management of hot work accidents.

Study on synergistic effect of mixed surfactants on wetting bituminous coal

LEI Miao, JING Deji, TONG Linquan, FAN Jingguang, JIA Xin, LIU Jianhua

2025, 35(3):  159-168.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0731

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In order to solve the problem that the traditional spray technology in coal mine dust pollution control is not ideal and the spray dust reduction performance is low, Sodium dodecyl sulfate (SDS), Coconut Diethanol Amide (CDEA) and Cocoamidopropyl Betaine(CAB-35) were selected in this paper. The wettability of the three surfactants was first analyzed macroscopically by contact angle test. Then, combined with molecular dynamics simulation and quantum chemical analysis, the action mechanism of single/compound surfactants on bituminous coal was studied from a microscopic perspective. The results show that among the single surfactants, SDS has the smallest contact angle and the largest molecular orbital energy difference, and is more likely to form high-strength hydrogen bonds with water molecules; the mixed surfactants all show better wettability of bituminous coal than single surfactants, and the contact angle is smaller when non-ionic surfactants are mixed with anionic or zwitterionic surfactants, and the contact angle reduction rate is also much greater than that of single solutions, and the synergistic effect is more prominent; In the CDEA+CAB-35 (4∶2) system, water and surfactants form more and higher strength hydrogen bonds, and the surfactant molecules pull each other to form a tight adsorption layer, which attracts water molecules to infiltrate the surface of coal dust and improves the wettability of coal dust to the best extent.

Bearing behavior of inclined pile for transmission towers in loess soils under immersion

ZHU Yulong, CAO Weiping, LYU Pin, WANG Yue, ZHAO Min

2025, 35(3):  169-178.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1128

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To improve the application of small-angle inclined piles in collapsible loess areas, finite element models of 2×2 pile groups with three different inclination angles were established. The modulus reduction method was employed to simulate the collapsibility effect of loess, and the influence of loading and soaking sequences on the bearing characteristics of pile groups with different inclination angles was analyzed. The results indicate that, based on the conducted tests of 0, 10 and 15° pile groups, under both loading-before-soaking and soaking-before-loading conditions, the displacement of the pile cap and the settlement of the surrounding foundation soil are smaller for inclined pile groups compared to vertical pile groups. Moreover, the inclined pile group is less affected by the water-induced collapsibility of loess. However, the bending moment and shear force of the inclined piles are higher than those of the vertical piles. Compared to the loading-before-soaking condition, the soaking-before-loading condition results in smaller pile cap displacements and soil settlements but larger internal forces in the inclined pile shafts. Additionally, the shaft friction of inclined piles is smaller under the soaking-before-loading condition. Inclined pile groups with larger inclination angles demonstrate superior load-bearing capacity and resistance to loess collapsibility induced by soaking. Pre-soaking treatment of loess foundations effectively enhances the ultimate bearing capacity of pile group foundations.

Public safety

Resilient prediction and dynamic spatial differentiation of core Chinese mainland cities

XU Hui, YE Zehong, ZHOU Qilin, ZHANG Rifen

2025, 35(3):  179-186.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0075

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In order to improve urban governance and promote sustainable development, the resilience measurement of core cities in Chinese mainland was analyzed based on panel data from 25 core cities (municipalities directly under the central government, provincial capitals, and regional capitals) between 2011 and 2020. Technique for order preference by similarity to an ideal solution(TOPSIS)-Entropy Weight Method was applied. The resilience situation for 2026 and 2029 was predicted using a BP neural network model. This research aimed to explore the dynamic spatial differentiation of regional resilience. The results show that the standard deviation of the resilience index across cities fluctuates around 0.180, with the resilience disparity between cities remaining relatively stable. However, some cities show a downward trend in their resilience index year by year. The standard deviation of the predicted resilience index for 2026 decreases to 0.173, indicating a reduction in the resilience disparity between cities and a narrowing of the resilience gap. In the four time points of 2014, 2020, 2026, and 2029, the spatial heterogeneity of urban resilience evolves relatively stably. The urban resilience rankings are as follows: Eastern region > Central region > Western region > Northeastern region. Among them, the economic and infrastructure resilience in the Eastern region is the highest, while the social and ecological resilience in the Central region is the highest.

Mathematical model of single-source multi-destinations evacuation and optimization of emergency exits

ZHOU Lingchang, LIANG Yin, LAI Yanping

2025, 35(3):  187-193.  doi:10.16265/j.cnki.issn1003-3033.2025.03.1994

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In order to improve the evacuation efficiency of public buildings and reduce casualties caused by emergencies, in view of the optimization problems of emergency exits and evacuation plans, a mathematical model and algorithm for the single-source multi-sink evacuation problem were proposed. Firstly, all evacuation paths were identified using path algorithm and sorted based on evacuation time. Then, the recursive formula for calculating evacuation time was derived based on the User Equilibrium (UE) principle and k-shortest path. The capacity allocation issues of each evacuation path at intersections were analyzed. After that then the optimization strategy for emergency exits is obtained. Finally, taking a building as an example, the evacuation network was established and calculated to validate the effectiveness of the algorithm and optimization strategy. The results show that the evacuation efficiency of the building network increases gradually with the increase in the number of evacuees, and the rate of change stabilizes after reaching a certain threshold value, Evacuation efficiency is closely associated with the total traffic capacity of specific emergency exits. Through the optimization of specific emergency exits, the evacuation efficiency of the network is enhanced, evacuation time can be reduced, and the optimization effect becomes more pronounced as the number of evacuees increases.

Elevator-assisted evacuation efficiency of deeply buried subway station under influence of multiple factors

HE Shanshan, WANG Qiao, CHEN Juan, YOU Yong, WANG Jinwei, MA Jian

2025, 35(3):  194-203.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0009

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To enhance the evacuation efficiency of deeply buried subway stations, a standard subway station was selected to establish an elevator-assisted evacuation model for deeply buried subway stations. The average evacuation time of passengers was selected as the primary evaluation metric. Variation characteristics in evacuation efficiency were calculated and analyzed under the combined influence of various factors, including buried depth of the subway, passenger flow intensity, the proportion of passengers opting for elevator evacuation, elevator operating parameters, the number of elevators, and acceptable queue size through simulation. The results indicate that the advantages of elevator-assisted evacuation are more pronounced when the subway depth exceeds 30 m. When passengers maintain their original evacuation paths, the evacuation time is inversely related to the proportion of passengers choosing to use the elevator during off-peak periods, but positively related during peak hours. Furthermore, when passengers alter their evacuation paths due to queue size, evacuation efficiency improves under different buried depth scenarios. In a subway with a burial depth of 90 m and an acceptable queue size of 30, the overall evacuation efficiency reaches its peak. When planning subway exits, reasonably increasing the number of elevators and their rated load, as well as operating speed, can effectively balance evacuation efficiency with cost control.

Gas knowledge bidirectional encoder representations from transformers model based on knowledge injection

LIU Xiaoyu, ZHUANG Yufeng, ZHAO Xinghao, WANG Kefan, ZHANG Guokai

2025, 35(3):  204-211.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0223

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In order to enhance emergency management in the field of gas pipeline networks, Gas-kBERT model was proposed. The model incorporated data from the gas pipeline network field expanded by Chat Generative Pre-Trained Transformer,(ChatGPT)and Chinese Gas Language Understanding Subject-Predicate-Object(CGLU-Spo) and related corpora were constructed in this field. By altering the model's masking (MASK) mechanism, domain knowledge was successfully injected into the model. Considering the professionalism and specificity of the gas pipeline network field, Gas-kBERT was pre-trained on various scales and contents of corpora and fine-tuned on named entity recognition and classification tasks within this field. Experimental results demonstrated that, compared to the general BERT model, Gas-kBERT exhibited significant performance improvements in F₁-score in text mining tasks in the gas pipeline network field. Specifically, in the named entity recognition task, the F₁-score was increased by 29.55%, and in the text classification task, the F₁-score improvement reached up to 83.33%. This study proves that the Gas-kBERT model performs exceptionally well in text mining tasks in the gas pipeline network field.

Gas pipeline network leak localization method based on transfer learning

CHEN Cen, JI Yubo, WANG Huan, NIE Rongshan, LIANG Xiaoyu

2025, 35(3):  212-220.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0957

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In order to enhance the reliability and safety of gas network operations and improve the fault diagnosis capabilities for gas network leaks, while addressing issues such as the scarcity of real gas network leak data samples and variations in operating conditions, a gas network leak localization method based on transfer learning was proposed. Firstly, the Random Forest feature importance ranking method was used to select five pressure monitoring points in the TGNET simulation network. Subsequently, pressure monitoring point data under three different pressure conditions were respectively used as the source domain and target domain input features. The traditional JDA method of transfer learning was improved to reduce the feature distance between the source domain and the target domain. Furthermore, the CS algorithm was employed to optimize the dimensionality after mapping d' and the learning rate λ parameters of the improved transfer learning algorithm, ultimately achieving the diagnosis of unlabeled target domain leak segments. The results indicated that the proposed leak localization method for complex gas networks can effectively improve the localization accuracy of unlabeled gas network leaks, achieving higher accuracy compared to traditional.

Technology and engineering of disaster prevention and mitigation

Scientific observation and early warning of extremely large reservoir landslides from perspective of emergency management

YE Xiao, ZHU Honghu, TIAN Kun, LI Houzhi, ZHANG Wei, CHENG Gang

2025, 35(3):  221-231.  doi:10.16265/j.cnki.issn1003-3033.2025.03.2006

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To enhance the ability to cope with reservoir landslide hazard risks under extreme climate, a framework for multi-dimensional scientific observation and hydrometeorological early warning was constructed using multi-source monitoring data and machine learning algorithms. The spatiotemporal pattern and main controlling factors of landslide deformation were identified by analyzing the multi-annual observations of the two landslide cases, involving Sentinel-1, global navigation satellite system (GNSS) surface displacement and fiber optic (FO) strain. Leveraging the boosting decision tree (BDT) algorithm, a hydrometeorological early warning method based on slip zone real-time strain (RTS) was proposed, and the generalized framework of monitoring, early warning and emergency management strategies for reservoir landslides was systematically discussed. The results indicate that landslides with different deformation mechanisms show different spatiotemporal deformation characteristics, and landslide activities are closely related to localized anti-sliding treatment measures. Landslide kinematics are characterized by subzone-independent displacements and their drivers, which are highly correlated with hydrometeorological extremes. The RTS-based early warning model provides specific hydrometeorological thresholds, emphasizing the emergency response-oriented landslide monitoring and early warning concept.

Emergency technology and management

Review of emergency supplies scheduling based on bibliometric analysis

YANG Qian, WANG Feiyue, WANG Zihuan, MA Bo, LU Jiajie

2025, 35(3):  232-241.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0680

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To investigate the research dynamics, hotspots, and frontier trends in the field of emergency supplies scheduling, the data sources of China National Knowledge Infrastructure (CNKI) and Web of Science(WoS) were used to search and filter 321 Chinese and 497 English articles. The bibliometric and knowledge mapping software were used to conduct basic feature analysis and development trend analysis. The results show that the number of articles in the field of emergency supplies scheduling both domestically and internationally has a wavy growth. The overall research is in the rapid development stage. In domestic core author teams, there is relatively low collaboration density. While in international contexts, cross-national and cross-regional academic exchanges are frequent, with China, Canada, and Singapore serving as the core. The research focus of both domestic and international studies is basically the same, mainly revolving around model design, optimization algorithms, path location issues, etc. However, international research has been earlier and more in-depth in studying the psychological perception of disaster victims.

Multi-objective dynamic program for emergency material allocation considering time window and fairness

SONG Han, CUI Na, ZHANG Yanping

2025, 35(3):  242-252.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0667

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During the attack of public health emergencies, the surge in material demand in isolated communities leads to a series of problems, i.e., serious shortfalls in supply, fairness of distribution and special distributional needs. To address this issue, a multi-period, multi-type, cross-regional emergency material dynamic allocation problem was studied based on a hierarchical distribution network of "material supply point-distribution center-isolated community". Especially, the demand time windows were applied to characterize the realistic requirements of isolated communities for the delivery time, and the special needs of some vulnerable groups, e.g., aging populations, were considered. Thus, a multi-objective non-linear dynamic program was developed by integrating the three dimensions of considerations: economy, material distribution satisfaction rate and distribution equity. In view of the multi-objective structure, the Epsilon constraint algorithm was used to solve the proposed model, and the material allocation during the attack of public health emergencies in Shanghai was taken as a case study to analyze the Pareto front of the optimal solutions and conduct the sensitivity analysis on some key parameters in the model. The results show that, under the constraint of limited emergency material supply, the material allocation strategy considering the aging populations helps to prioritize the material supply of vulnerable groups. However, it also contributes to an increase in the unfairness rate of the isolated communities as a whole. For the material allocation strategy considering the delivery time windows of isolated communities, the total operation cost of the cross-regional distribution network and the unmet rate of the material demand in the isolated communities are high, but the overall distribution of supplies in the system is relatively fair, and it could better meet the needs of isolated communities in terms of delivery of supplies, which provides a useful reference for the decision makers to balance the individual demand and the overall interests.

Occupational health

Development and validation of a coupled multi-layered heat and mass transfer model of firefighting protective clothing

YANG Jie, LU Yingru, LEI Ying

2025, 35(3):  253-260.  doi:10.16265/j.cnki.issn1003-3033.2025.03.0531

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To improve the accuracy of human thermal injury assessment and protect rescuers' safety in thermal radiation environments, a coupled heat and mass transfer model of skin-microenvironment-firefighting clothing system was proposed to predict skin burn injuries under dynamic conditions. Based on mechanism of heat and moisture transfer in porous media, the periodic movement of fabric caused by human activities and its impact on heat and mass transfer in the skin-microenvironment-firefighting clothing system were considered. Furthermore, the proposed model was used to simulate skin temperature, time of skin burn, and the distribution of temperature and humidity in the fabric layers for both dry and wet cases in real time. The results show that the relative error between simulated values predicted by the model and the experimental measurements presented in the literature is only 3.79%. When exposed to 8.5 kW/m² thermal radiation environments, the time to second-degree burn for the dry case is 33.7 s earlier than that for the wet case. When firefighters approach a 20 kW/m² radiant heat source at a speed of 1 m/s, the heat transferred is impeded by the increase in thermal layer thickness. This extends the time for second-degree burns to occur by 10.9 s and reduces the heat absorbed by the skin surface by 20%. When the air gap thickness in the microenvironment is the same as the amplitude of the periodic motion of fabric, the skin temperature increases rapidly and fluctuates significantly, and the time to second-degree burn occurs 43.7 s earlier. Human body movement and moisture in fabric layers affect heat transfer process between the human body and thermal environment, thereby their impact on the accuracy of rescue assessments cannot be ignored.