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About the Journal
Superintended by：China North Industries Group Corporation Limited
Sponsoredy：Beijing Institute of Technology, Chinese Scoiety for Environmental Sciences, China Occupational Safety and Health Association
Edited & Published by: Editorial Department of Journal of Safety and Environment
Issues per year: 12
ISSN 1009-6094
CN 11-4537/X
Identification of risk coupling factors and key pathways for remotely controlled vessel grounding
FAN Cunlong;QIU Yuhui;YIN Chuanzhong;XI Yongtao;HU Shenping;HAN Bing;While intelligent shipping is an inevitable trend, the coupling mechanisms of grounding risks for remotely controlled vessels are still not well understood. To address this gap, a multi-dimensional sensitivity analysis is conducted to identify critical factors contributing to the risk coupling effects associated with smart vessel grounding. The analysis focuses on 98 grounding accident reports from the western waters of Shenzhen Port, categorizing them into sub-samples based on the time of occurrence to align with the respective shifts of the chief, second, and third officers. Risk coupling evaluation is performed using the 24Model and N-K model to quantify the coupling effects across five types of risk factors: Human-related, Organization-related, Ship-related, Environment-related, and Technology-related, as well as their various combinations. Paired rank tests, interval number ranking, and sensitivity analysis are conducted to identify key risk factor types, assess the influence of the number of combined risk factors, determine key risk coupling pathways, and analyze differences among subsamples. The number of accidents is incorporated as a new input parameter for the N-K model, and risk coupling values are recalculated accordingly. A sensitivity analysis is then performed to determine the relative significance of each factor in relation to the overall risk coupling effects. The results indicate that the risk coupling effects among the chief, second, and third officers during their work shifts show considerable similarity across various combinations of risk factor types when remotely controlled vessels operate in port waters. However, the sensitivity of the risk coupling values varies across the three work shifts under multi-factor complex coupling scenarios. The risk coupling values exhibit significant sensitivity to increases in the number of accidents. Sensitivity analysis confirms that the impact of changes in the number of accidents associated with different risk factor types on risk coupling effects is characterized by nonlinearity. While Human-related, Ship-related, and Technology-related factors are crucial when considered in isolation, their individual significance diminishes in comparison to scenarios involving multiple risk factor combinations. Specifically, individual Ship-related or Technology-related factors demonstrate limited importance; however, their combined influence significantly enhances the sensitivity of risk coupling values, thereby revealing the key risk coupling pathways. In complex coupling scenarios, it is essential to mitigate Human-related risks to prevent the amplification of the risk coupling effect.
Dynamic-evasive risk assessment for mitigating conflicts between pedestrians and e-bicycles
WANG Chen;WANG Shuai;XIAO Changjiang;DUAN Songzhi;ZHONG Jingyang;This study introduces a novel method for assessing the severity of conflicts between pedestrians and e-bicycles by integrating dynamic behavioral indicators with traditional motion-based safety metrics. A total of 843 conflict events were identified from 10 hours of aerial and ground video footage collected across three urban road segments in Xi'an, China. Based on the timing of pedestrian evasive responses, the conflicts were classified into three categories: early evasion, emergency evasion, and no evasion. To quantify the intensity of pedestrian movements during conflicts, a keypoint-based Body Change Magnitude(BCM) index was developed. The BCM integrates angular displacement and angular velocity derived from 17 human body keypoints detected using the Yolov8n-pose model. A dynamic weighting scheme, based on sigmoid functions, was employed to merge these two components. Additionally, the Post Encroachment Time(PET) and Deceleration to Safety Time(DST) were calculated using trajectory and speed data for both pedestrians and e-bikes. Together, these three indicators formed the input features for risk assessment. A modified K-means clustering algorithm—utilizing probabilistic initialization and Euclidean distance minimization—was employed to classify conflict severity into three levels: minor, moderate, and severe. Subsequently, a Back Propagation Neural Network(BPNN) with one hidden layer containing four neurons and a sigmoid activation function was used to predict these severity levels. The model was trained on 80% of the samples and tested on the remaining 20%, successfully converging within 1 000 iterations at a learning rate of 0.1. The proposed model achieved an overall prediction accuracy of 81.7%, with the moderate severity category attaining a precision of 82.43%. Emergency evasion cases demonstrated the highest average BCM at 0.622 rad, the shortest PET at 1.440 s, and the highest DST at 5.310 m/s2; notably, 50.4% of these cases were classified as severe. Importance analysis indicated that BCM and relative velocity were the two most influential factors, with contribution rates of 92.8% and 90.3%, respectively. The primary innovation of this study is the introduction of BCM—a dynamic behavior indicator that effectively captures both the intensity and timing of evasive actions—thereby enhancing conflict severity prediction and providing a new perspective for pedestrian risk assessment in complex urban traffic environments.
Research on risk assessment method for urban new power system under extreme weather conditions
LIU Chang;SUN Qiujie;ZHANG Xinwei;LI Peng;ZHOU Hui;ZHANG Sihang;LI Junhui;As the proportion of new energy sources continues to rise, the power system faces significant uncertainties in generation, transmission, load, and storage. Coupled with the impact of extreme weather events such as typhoons and heavy rainfall, new urban power systems encounter multiple challenges in identifying and quantitatively assessing risks. Traditional risk assessment methods are limited in their ability to analyze multiple risks and their interconnections quantitatively. Therefore, this paper proposes a risk assessment method for new urban power systems under extreme weather conditions by combining knowledge graphs with the PageRank algorithm. First, case studies of power system failures due to extreme weather serve as the data source for knowledge extraction, utilizing a combination of rule-based and deep learning approaches. A risk knowledge graph is then constructed using Neo4j, incorporating five types of attributes: ‘disaster type', ‘failure type', ‘accident type', ‘accident location', and ‘accident consequences'. Next, the PageRank algorithm and degree centrality metrics are employed to analyze key risk nodes within the urban power system's risk knowledge graph. In the case of accident types, the PageRank value for equipment body damage is 1.025 5, and the betweenness centrality is 669.07. Both indicators being high suggests that the impact of this accident is widespread and that it lies on a critical path in the risk evolution process. Among failure types, the betweenness centrality for disconnection is relatively high at 32.05, while the PageRank value is relatively low at 0.294 3, indicating that this fault has a low probability of occurrence but possesses high propagation influence. Additionally, overhead lines emerge as the most critical failure points in the new power system. Therefore, enhancing the monitoring of transmission line status and improving their disaster resistance capabilities are essential.
Investigating the coupling of explosion flame emission spectra and explosion pressure in metal powder reactions
MENG Xiangbao;YUAN Jun;SONG Shizemin;GUO Weixiao;LI Dengzhao;LIU Shanshan;To facilitate the timely detection of metal powder explosion accidents and provide a theoretical foundation for the rapid response of explosion suppression equipment, this study explored the dynamic coupling relationship between flame emission spectra and pressure parameters during explosions of aluminum powder, magnesium powder, and aluminum-magnesium alloy powder(particle size: 10-75 μm). Experiments were conducted using an Andor Shamrock 500i fiber optic spectrometer(acquisition rate: 143 frames per second, grating: 500 nm) in conjunction with a 20 L powder explosion tester. The powder samples were dispersed into a container that had been pre-evacuated to 32 kPa, with a 60 ms ignition delay. Spectral data were collected at a distance of 400 mm from the observation window. Key results revealed distinct spectral features: aluminum powder explosions exhibited a dominant AlO characteristic peak at 511 nm, in contrast to the 486.3 nm peak observed during combustion. The peak intensity was inversely proportional to particle size, with intensity values around 6 000 for 10 μm particles compared to approximately 2 000 for 50 μm particles. In magnesium powder explosions, a doublet peak was noted at 500 nm(MgO) and 518 nm(atomic Mg), with the intensity of the 518 nm peak being 40% higher for 20 μm powder compared to 50 μm powder. Aluminum-magnesium alloy powder explosions exhibited a prominent magnesium peak at 518 nm, with no detectable AlO emission. Analysis revealed that the spectral peaks consistently appeared 10-50 ms before the maximum pressure(pmax). Notably, we identified a fixed sequence: the maximum pressure rise rate(dp/dt)max occurred first, followed by the spectral peak, and finally pmax. Additionally, the pressure rise rate significantly decreased with increasing particle size, for example, from 10 MPa/s for 10 μm aluminum to 2.4 MPa/s for 50 μm aluminum. Above 0.2 MPa pressure, the radical spectral responses were rapid, while the pressure rise exhibited a lag due to gas expansion. This study identifies 511 nm(aluminum powder), 500 nm(magnesium powder), and 518 nm(aluminum-magnesium alloy powder) as optimal detection wavelengths. The strong correlation between spectral intensity and pressure during the rising phase, along with the lead time of(dp/dt)max and spectral peaks over pmax, establishes a foundation for early explosion detection.
Fire spread behavior of underwater oil releases: effects of leakage rate and water depth
LIU Jiaohao;LI Xiang;WANG Kai;With the rapid advancement of offshore oil and gas development, incidents of submarine pipeline leaks have become increasingly common, posing significant threats to the marine environment, offshore operations, maritime transportation, and industrial activities. To investigate the disaster characteristics associated with underwater oil spills and the resulting surface fires, this study employs small-scale experiments that simulate fire accidents caused by subsea oil pipeline leaks. The research specifically examines how the oil leakage rate and the depth of the leak orifice influence fire spread dynamics, focusing on the evolution of the spreading radius and flame height during surface fire propagation. Controlled experiments were conducted under varying leakage rates and orifice depths to replicate realistic scenarios. Key findings reveal distinct patterns in fire development: both the peak spreading radius of the surface fire and the time required to reach a stable burning stage significantly increase with greater leak orifice depth. However, during the stable combustion phase, the spreading radius does not exhibit a clear correlation with leak hole depth. In contrast, flame height during the stable burning stage shows a positive correlation with both the oil leakage rate and the leak hole depth. Higher leakage rates provide more fuel to the fire, while greater depths affect the initial dynamics of oil surfacing and potential pre-mixing. Building on these observations, a predictive model for flame height has been developed. This model incorporates the oil leakage rate and leak hole depth through a modified dimensionless heat release rate parameter, which accounts for the specific influence of the submerged release point on combustion processes. The model establishes a quantitative relationship between key input variables and the resultant flame height during stable burning. This study offers valuable experimental data and a foundational predictive framework for understanding the hazard evolution of fires triggered by underwater hydrocarbon leaks. The established relationships between leak parameters and fire characteristics enhance risk assessment methodologies. Additionally, the flame height model serves as a practical tool for estimating fire intensity during the stable phase, aiding in the preliminary design of safety measures and emergency response strategies for offshore oil and gas infrastructure. Future research could further investigate scaling effects, various oil types, and the impact of ocean currents.
