Table of Content

28 December 2024, Volume 34 Issue 12

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Safety social science and safety management Safety engineering technology Public safety Emergency technology and management

Safety social science and safety management

Impact of implicit and explicit safety attitudes on safety behavior among construction workers

YE Gui, YUE Hongzhe, YE Xin

2024, 34(12):  2-7.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0222

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To improve construction workers' safety behavior and enhance the effectiveness of both implicit and explicit safety attitudes on this behavior, this study investigated the interaction between these two types of attitudes and their combined influence on safety behavior. First, an experiment was designed to measure the implicit safety attitudes of construction workers, and Implicit Association Test(IAT) was used to evaluate underlying attitudes. Then, the relationship between implicit and explicit safety attitudes was analyzed based on an explicit safety attitude scale. Finally, the study examined how the three components of both implicit and explicit safety attitudes—cognitive, emotional, and behavioral tendency—affected safety behavior. The results show that construction workers generally exhibit positive implicit safety attitude. However, the correlation between implicit and explicit safety attitudes is weak. Explicit safety attitude, particularly the overall, emotional, and behavioral components, has a significant positive effect on safety behavior, while the correlation between implicit attitude and safety behavior remains weak. When implicit and explicit safety attitudes are aligned, their correlation with and explanatory power for safety behavior increases.

Classification and management method of errors and violations in civil aviation pilots' unsafe behaviors

WANG Lei, WEI Zixin, ZOU Ying

2024, 34(12):  8-15.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0281

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To address the classification and quantitative evaluation issues of unsafe behaviors of civil aviation pilots, the management mode of their unsafe behaviors was optimized. A method for managing unsafe behaviors in civil aviation was proposed focusing on the civil aviation flight field. Firstly, based on unsafe behavior classification theory, intervention and improvement methods for errors and violations were systematically analyzed to distinguish between errors and violations. Secondly, expert interviews and questionnaire surveys were used to propose a classification evaluation index system for pilots' unsafe behaviors. Furthermore, a classification method of unsafe behavior based on quick access recorder (QAR) data and a quantification method of unsafe behavior scores based on flight operations quality assurance (FOQA) were proposed to achieve a classified quantitative assessment of pilots' unsafe behaviors. Finally, the classifying and managing unsafe behavior process was analyzed and validated. The results indicated that the 68 FOQA monitoring events obtained by screening and calculation had different causal behavioral tendencies. The two proposed unsafe behavior classifications and quantitative evaluation methods can be combined with QAR data or FOQA records. Moreover, the classification management of unsafe behaviors of civil aviation pilots can be achieved by combining intervention and improvement measures for errors and violations.

Case decision study of coal mine safety input based on DS evidence-regret theory

JIANG Fuchuan, ZHANG Siyu, ZHANG Guoqing, NIU Yue, LI Menglin, LIU Peishun

2024, 34(12):  16-23.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0429

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In order to achieve a more comprehensive and effective investment in coal mine safety, aiming at the influence of psychological and behavioral factors of decision makers on safety input decisions, a regret theory-CBDT safety input decision model was established. By analyzing the applicability of CBDT in coal mine safety input, safety input decision-making index system was established from two aspects of safety input and safety output, based on improved DS evidence theory and the evaluation of experts, and the weights of each indicator were calculated. In the CBDT perspective, based on regret theory, the comprehensive perceived utility of each scheme was calculated, and the optimal safety investment scheme was selected according to the comprehensive perceived utility. The result shows that in the process of safety input, policymakers pay more attention to safety facilities and safety technology input, and policymakers will try to avoid safety input schemes with unsatisfactory safety output. The model is based on historical safety investment cases and combines the subjective psychology of decision-makers, it helps select the optimal safety input scheme, makes safety investment decision-making more objective and scientific.

Generation mechanism of weak prevention in process of accident risk governance

ZHANG Jian, HU Chunyan

2024, 34(12):  24-33.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0580

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In order to promote the precise governance goals of accident risks, a research method combining process-tracking based on actor network theory and case comparative analysis was adopted to study the weak prevention generation mechanism in the process of accident risk governance. Firstly, the technical environment for analyzing the evolution process of accident risk was clarified, including prepositive contexts, structural scenarios, and developmental circumstances, as well as the analysis logic based on Lens Model and dimensions of time, space, and structure, to provide a research foundation for the integrity analysis of accident risk production process. Secondly, based on the case comparative analysis of the tracking of the interaction process between different types of actors and risks, the role change picture of the core actors, homeowners and rainstorm, in the process of accident/disaster risk governance and accident/disaster generation was presented integrally. Finally, a comparative analysis and reflection of case studies based on tracking the interaction between different types of actors and risks were summarized, and the essence and generation mechanism of weak prevention in the process of accident risk governance were proposed. The results indicate that the mixed interaction between human and non-human actors can continuously activate the emergence and mutual construction process of new actors, new intersectionality, and new vulnerabilities in the system, subsequently resulting in the non-stationary evolution of accident risk production environment and risk structure, which leads to intervention failure and recurrence of weak prevention in the process of accident risk governance.

Safety engineering technology

Research progress on influence mechanism of aging on wood combustion characteristics

WANG Kai, HAO Danping, ZHOU Biao, JIANG Chenyang

2024, 34(12):  34-39.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0083

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To protect wooden buildings from fire threat, the influence mechanism of aging on wood burning characteristics was discussed. Firstly, the combustion behavior of wood was systematically discussed in four aspects: pyrolysis, flame combustion, smoldering combustion and flame spread. Secondly, the combustion characteristics of naturally aged wood and artificially accelerated aged wood were compared and analyzed. Finally, the effects of aging on wood fire risk were analyzed based on the smoke generation characteristics of wood combustion and the flame-spreading behavior of wood structure buildings. The results show that the mechanical properties of wood are significantly reduced by changing the internal composition and carbonization degree of wood, thus weakening the fire resistance and affecting the smoke generation. At the same time, the changes in physical and chemical properties and structural characteristics of aged wood promote the fire spread of ancient wooden buildings in the initial stage of fire. However, the study on the mechanism of wood carbonization by aging is still insufficient, and the influence of aging mode and environmental conditions on the dynamic characteristics of fire at the later stage of combustion has not been clarified. To evaluate the impact of aging on the fire risk of ancient wooden buildings, it is essential to integrate material science and structural mechanics for effective fire safety measures.

Semantic matching model of potential safety hazards in hydroelectric project construction

CHEN Shu, WANG Dianxue, YANG Yingliu, CAO Kunyu, NIE Benwu

2024, 34(12):  40-47.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0795

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In order to assist in the development of safety hazard management measures for hydropower project construction, the safety hazard texts accumulated during the construction inspection of hydropower projects were collected. Entities and relationships from the semi-structured safety hazard texts were extracted using Python. A knowledge graph of safety hazards was constructed and imported into the neo4j graph database for storage. A Sentence-Bidirectional Encoder Representations from Transformer (BERT) model based on bidirectional coding was built for the semantic matching of construction hazards in hydropower projects. The deep semantic features of target hazards and historical hazards were learned, and the historical safety hazards most similar to target hazards were recommended. Using the Cypher query statement, the governance measures corresponding to the historical security risk were searched. The results show that the Sentence-BERT model has an accuracy of 96.48% in identifying architecturally and historically similar safety hazards, which is significantly better than BERT, Word2vec-Deep Semantic Similarity Model (Word2vec-DSSM), and BERT-DSSM models. Among 150 randomly selected target safety hazard data, the accuracy rate of testing historical similar safety hazard suggestions reaches 92%, and the retrieval effect of hazard management measures is demonstrated through the hazard knowledge graph, which verifies the applicability and effectiveness of the method.

Prediction model of pilot maneuver stability based on LSTM

WANG Wenchao, HE Jian, SONG Baisheng, WANG Lei

2024, 34(12):  48-55.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0145

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To predict unsafe events for pilots in real time, a LSTM neural network was used to assess pilot maneuver stability and pilot maneuvering quality was improved by optimizing indicators. Firstly, a set of human-machine maneuvering factors presenting the pilot's maneuvering behavior characteristics was proposed by analyzing the pilot's stability maneuvering QAR data in flight. Secondly, the factors affecting the stability maneuvering of the aircraft were analyzed, and a gray correlation analysis method was used to determine the 15 characteristic parameters of associated risks from the 37 monitoring parameters closely related to the stability of the aircraft. Then, the LSTM model was used to train and test the data to predict the pilot's maneuvering stability, and indicators were proposed to evaluate safety stability quality. Finally, ML was used to rank the importance of relevant influencing factors to improve model validity. The results indicated that the time series model effectively eliminated the interference of parameters with little or no correlation with the prediction results in the original parameters. The stability model can predict risks with high accuracy and provide pilots with a 3-4 s time margin to take preventive measures and reduce unsafe incident occurrence during flight.

Tunnel construction safety domain named entity recognition based on BERT-BiLSTM-CRF

ZHANG Nian, ZHOU Caifeng, WAN Fei, LIU Fei, WANG Yaoyao, XU Dongliang

2024, 34(12):  56-63.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0713

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To solve the problems existing in the traditional NER methods in the domain of tunnel construction safety, such as fuzzy entity boundary, difficulty in small-sample learning, and insufficiently comprehensive extraction of feature information, an entity recognition method for tunnel construction accident text based on the BERT-BiLSTM-CRF model was proposed. Firstly, the BERT model was used to encode the tunnel construction accident text to obtain word vectors containing semantic features. Then, the word vectors output after the training of the BERT model were input into the BiLSTM model to further obtain the context feature of the tunnel construction accident text and conduct label probability prediction. Finally, by utilizing the constraints of the annotation rules of the CRF layer, the output result of the BiLSTM model was corrected, and the maximum probability sequence annotation result was obtained, so as to realize the intelligent classification of the labels of the tunnel construction accident texts. Comparative experiments were conducted between this model and other four commonly used traditional NER models on the tunnel construction safety accident corpus dataset. The results show that the recognition accuracy rate, recall rate and F₁ value of the BERT-BiLSTM-CRF model are 88%, 89% and 88% respectively, and the entity recognition effect is better than other benchmark models. By using the established NER model to recognize the entities in the actual tunnel construction accident texts, its application effect in the domain of tunnel construction safety is verified.

Research on collision risk of paired approach based on fuzzy Bayesian and event tree

LU Fei, ZHANG Jian, ZHAO Erli

2024, 34(12):  64-73.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0662

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In order to assess the collision risk of paired approach to closely spaced parallel runways, a collision risk model based on fuzzy Bayesian and event tree analysis was developed. Initially, the paired approach procedure was delineated, and risk factors inherent in it were identified through the Failure Mode and Effect Analysis (FMEA) method. Subsequently, a Bayesian network model addressing hazards during the paired approach and potential control failures was constructed, leveraging the identified risk factors. The probability of the root node was determined through a combination of expert survey weighting and statistical analysis of historical data. Some root node probobilities and the conditional probability of the intermediate nodes were fuzzified utilizing seven-level linguistic variables, followed by de-fuzzification using the incentre of area. Additionally, priori probabilities and sample data were input into BN software for expectation-maximization(EM)parameter learning, facilitating the determination of hazard proximity and control failure probabilities. Lastly, considering the time series relationship between hazardous approach, control failure, and collision events, the collision risk associated with paired approaches was assessed employing event tree analysis, and a sensitivity analysis was conducted on the BN. The result shows that hazardous approach sensitivity is highest to pilot operating level, while control failure sensitivity is highest to poor maintenance. If the probability of poor pilot performance surpasses 12%, and the likelihood of inadequate maintenance surpasses 0.17%, the paired approach operation fails to meet the safety target level.

Research on 4D flight trajectory prediction based on improved Transformer model

LIU Hong, ZHANG Xindi, LU Fei, ZHANG Chengyu

2024, 34(12):  74-83.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0497

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Flight trajectory prediction plays a crucial role in ensuring safe and efficient air traffic operation. In order to consider the implicit correlations between flight trajectory characteristics, the encoding and decoding operations of the prediction framework in the transformer model were enhanced. Firstly, the convolutional block was improved, and ordinary convolutions were applied to capture the correlations between neighboring time series trajectory characteristics, and dilated convolutions were added to capture correlations between non-neighboring time series trajectory characteristics. Secondly, multi-head self-attention was employed to perform calculation based on the spatiotemporal features of the flight trajectory combined with the importance of attention scores. Thirdly, probabilistic sparse method was designed to reduce the computational complexity of the multi-head self-attention and improve the model's computational efficiency. Finally, an experimental platform was established to verify the flight trajectory prediction framework. The results show that compared to the traditional transformer model and the other three neural network models, the improved transformer model shows a 14.4% improvement in time performance. By using root mean square error(RMSE) and mean absolute error(MAE) as evaluation metrics, the average prediction deviations of the improved transformer model for trajectory features such as longitude, latitude, and altitude are 0.027 and 0.021, respectively. These deviations are reduced by 0.072 and 0.063 compared to the traditional transformer model's average prediction deviations of 0.099 and 0.084. Sensitivity analysis on the lengths of prediction sequences indicates that the improved transformer model is more stable than the baseline models.

Active steering control strategy for VRU safety under mixed conditions

HONG Liang, CHEN Zhihao, LI Liang

2024, 34(12):  84-93.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0252

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This study investigated the protective effect of an active steering control strategy when VRU start from the outside or inside of a curve and cross the road with uniform acceleration, deceleration, and uniform velocity. Firstly, the spatial positional relationship models for vehicles and VRU, the safety assessment models, and the active steering safety distance models were established to propose an active steering control strategy. Then, a lateral collision avoidance controller was designed using the quintic polynomial lane-change method, the Frenet coordinate transformation method, and the model predictive control method. Finally, with electric bicycle riders as the collision avoidance targets, 18 mixed conditions were constructed based on the state of the target lane status, the movement direction, and the speed of the electric bicycle to verify the collision avoidance effect of the active steering control strategy. The results indicated the active steering control strategy avoided collisions between vehicles and electric bicycles under mixed conditions. The preceding, ego, and following vehicles in the target lane can drive normally during lane-changing, and the vehicle ride comfort was satisfactory.

Intelligent analysis of building fire accidents based on knowledge graph

XU Hui, JIANG Mei, XUE Hong, ZHOU Qilin

2024, 34(12):  94-99.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0688

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To provide intelligent and systematic decision support for building safety management, building fire accidents data was collected and summarized. The knowledge graph of building fire accidents was developed to construct a knowledge graph database. Based on the dimensions of time, space, theme, and important entities, the implementation process of the intelligent question-answering system was innovatively presented. Moreover, the intelligent analysis of building fire risk was performed. The results showed that daytime and summer were high-risk periods for building fires. The frequency of building fire accidents in East China was significantly higher than that in other regions, and the fire risk of building fires was higher in electrical and warehouse areas. Reinforced concrete frame structures and factory buildings were more prone to building fires. Most ignition sources were combustible solids, and the main cause of fire accidents was illegal construction behavior.

Failure analysis and improvement measures of hydrogenation reactor flange sealing groove

ZHANG Chunyi, ZHU Ge, TIAN Zhiyong, SONG Ming, DU Liang, WU Yuanjian

2024, 34(12):  100-107.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0102

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In order to prevent accidents caused by the failure of the flange sealing groove in the hydrogenation reactor, failure analysis methods such as macroscopic inspection, fracture analysis, ferrite detection, chemical composition analysis, hardness detection, metallographic detection, SEM analysis and operation process analysis were adopted. The influencing factors of damage mode, start-stop operation process, ferrite content in the weld overlay layer, material properties, and abnormal elements were studied, and the reasons for the failure of the flange sealing groove at the outlet of the hydrogenation reactor were analyzed. The results indicate that the failure of the sealing groove is mainly due to the high stress between the weld overlay layer and base metal of the sealing groove, which produces stress corrosion cracking under the action of a corrosive medium containing F, S and other elements. There is no weld overlay layer on the bottom surface of the cracked flange sealing groove, and the transgranular cracks started at the junction of the surface weld overlay layer and the non-weld overlay layer, mainly on the surface of the non-weld overlay layer side. According to the failure causes, the corresponding improvement measures are put forward from the aspects of manufacturing, material selection and maintenance.

Mechanism and recognition of TBM roof fall in main inclined shaft of Kekegai mine

YANG Qing, RONG Chuanxin

2024, 34(12):  108-119.  doi:10.16265/j.cnki.issn1003-3033.2024.12.1018

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To explore the approach for dealing with roof fall by the open TBM in coal mine excavation, the mechanism and pattern recognition of roof fall were investigated considering the unfavorable geological conditions such as abundant water, faults, joints and sandstone with fractured structure. Firstly, the roof fall mechanism was analyzed by utilizing the modified excavation compensation theory and the minimum support stress of surrounding rock which fully considered the intermediate principal stress. Based on the successful case of the main inclined shaft of Kekegai mine and TBM site construction data, the characteristics of roof fall were deeply analyzed. Then, in accordance with the collected on-site feedback monitoring information, the variations of excavation parameters before and after the roof fall were systematically examined, and the machine learning models of random forest (RF), back propagation (BP) neural network, and Library for support vector machines (LIBSVM) were constructed to effectively identify the roof fall. The results demonstrate that the internal cause of roof fall is the deterioration of sandstone mechanical properties resulting from water-rock interlace in the cataclastic structure, the external cause is the energy release by mechanic-rock action, and the controlling cause is the excavation stress compensation and the timely application of steel anchor (cable) shotcrete + steel arch (steel plate belt). The sharp increase of penetration, and thrust of the hob, the torque of the cutter head and sharp decline of the cutter head speed are the characteristics of roof fall driving parameters. The RF model has the highest prediction accuracy for the classification of roof fall of surrounding rock, and its accuracy rate of identifying roof fall risk is 1.78% and 11.84% higher than that of BP and LIBSVM, respectively.

Accident injuries model of ship repair and building enterprises based on binary Logistic regression

LI Xian, JIAO Yu, SHI Danda, WU Jianjun, KANG Yutao

2024, 34(12):  120-128.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0350

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In order to explore the factors influencing accident injuries in SRBE, a binary LR model was constructed based on data from 1 411 accidents from a Chinese shipbuilding group. OR were used to quantify the impact of factors such as enterprise, time, location, personnel, environment, and accident types to accident injuries. The result identifies 11 significant factors influencing accident injuries. Non-contract workers face significantly higher risks of injury compared to contract workers. Non-approved hazardous operations pose 3.246 times the risk of accident injuries compared to approved hazardous operations. Male perpetrators present a significantly higher risk of causing accident injuries than female perpetrators. The education level of the workers is predominantly at junior high school, and higher education levels are associated with a lower risk of causing accident injuries compared to those with junior high school education. Accident injuries exhibit seasonal characteristics, with accidents occurring frequently in the second quarter, while relatively fewer and less risky in the first quarter. Peak working hours and workdays significantly increase injury risks compared to off-peak hours and non-working days. Object strikes are the primary type of accidents, and mechanical injuries significantly increase the risk of accident injuries. Ships and workshops are the most likely locations for accident injuries within the shipyards. Longer years of service reduce the risk of accident injuries, while higher temperatures increase it.

Effect evaluation and experimental validation of engineering blasting based on extension-analytic hierarchy process model

NI Suqian, XU Ying, YANG Rongzhou, YAO Xiangyang, YUAN Yanwei, DING Jinfu

2024, 34(12):  129-139.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0509

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In order to ensure the normal passage of vehicles and the safety of the existing tunnel support structure during the blasting through the highway, the evaluation method of engineering blasting effect based on extension-AHP model was proposed. Firstly, by means of investigation and analysis, the blasting effect rating standard and index system were established, and the model was applied to the evaluation of a water diversion project. Secondly, AHP was used to determine the weights of evaluation indexes, and the combined relevance degree of blasting rating was calculated. Finally, the results of the blasting effect rating were verified by acoustic detection test, blasting shock wave test and blasting seismic wave test. The study shows that the combined relevance degree {{{{{Q_j}_{(}}_X}_{-}}_M}_{)} is calculated by extension-AHP model. The blasting effect of the tunnel boring is Q_max=-0.017, and the evaluation grade is a good blasting effect. The surrounding rock loose circle of the tunnel is relatively small and evenly distributed. The influence range of the surrounding rock stability is about 0.5-0.6 m. The blasting energy does not cause the rock rupture zone to further extend the signs of the inward. The energy attenuation trend of blasting seismic waves is different under different wave frequencies. However, the attenuation rate is greater than that of low-frequency component energy in the overall performance of high-frequency component energy. In the same channel, with the increase of the distance between the blasting source and the measurement points, the overall vibration waveform becomes narrower. The main frequency increases first and then decreases, and the main frequency domain moves to the low-frequency direction. The overpressure peak attenuation characteristic of blasting shock wave meets P_S=αl^-γ. With the increase in the distance from the blasting source, blasting shock wave overpressure attenuation coefficient is an increasing trend. The measurement range belongs to the shock wave attenuation zone. The shock wave overpressure peak of the tunnel entrance and the construction outside tend to converge.

Tunnel rockburst prediction based on LLE-DBSCAN-SMOTE data processing

FAN Chengqiang, XIA Yuanyou, ZHANG Hongwei, HUANG Jian

2024, 34(12):  140-148.  doi:10.16265/j.cnki.issn1003-3033.2024.12.1917

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To address issues of correlation prediction indicators, outliers, and data imbalance in original data in rockburst prediction, a rockburst prediction method based on LLE-DBSCAN-SMOTE for data processing was proposed. Firstly, the maximum tangential stress of surrounding rock {\sigma }_{\mathrm{\theta }}, uniaxial compressive strength of rock {\sigma }_{\mathrm{c}}, uniaxial tensile strength of rock {\sigma }_{\mathrm{t}}, elastic strain energy index W_{\mathrm{e}\mathrm{t}}, brittle coefficient {\sigma }_{\mathrm{c}} / {\sigma }_{\mathrm{t}}, stress coefficient {\sigma }_{\mathrm{\theta }} / {\sigma }_{\mathrm{c}}, and stress concentration value β characterizing the stress gradient of surrounding rock were selected to construct a rockburst prediction indicator system. Secondly, the LLE algorithm was used for data dimensionality reduction to eliminate the cross-correlation effect between indicators, and the DBSCAN algorithm was introduced to remove outliers. Then, the SMOTE technology was introduced for data balancing. Finally, three types of rockburst prediction models were proposed using Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) algorithms. The prediction accuracy of the data training models before and after processing was compared and analyzed. Moreover, engineering verification was performed through the measurement in the diversion tunnel of Jiangbian Hydropower Station. The results show that the prediction accuracy of the three types of algorithm models which reduce the prediction index from the 7 dimensions of the original data to the 4 dimensions and adopt the graded outlier processing is the highest among the similar models. The rockburst prediction of the Jiangbian Hydropower Station demonstrates that the proposed model significantly improves prediction accuracy compared to similar models using original rockburst data.

Characteristics of coalbed gas seepage under asymmetric loading and optimization of extraction radius

YAN Lu, WANG Tao, WANG Liancong, LI Wenpu, ZHAO Tianwei

2024, 34(12):  149-158.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0905

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To reveal the influence of asymmetric load on gas seepage and extraction radius, a multi-physics coal and gas fluid-solid coupling model was proposed to analyze the gas seepage characteristics of coal seams. Matrix-adsorbed gas was used as the mass source in the proposed model introducing asymmetric loads into the boundary conditions. Furthermore, segmented drilling was used under asymmetric load conditions to optimize the gas extraction radius and improve extraction efficiency. The results indicated that greater stress compressed the cracks inside the concentrated stress zone, making it more difficult for gas to flow and to be extracted more challenging. The gas pressure in the concentrated stress zone decreased by approximately 2% less than that in the original stress zone, and the permeability decreased by about 9%. Asymmetric load had different degrees of influence on the diffusion and seepage processes. Within 180 days, the mass of diffused gas of the original stress area decreased by 19% and the seepage mass decreased by 20.5%, while these values in the concentrated stress zone decreased by 16.9% and 17.9%, respectively. Asymmetric loads had adverse effects on gas extraction, increasing extraction time under uniform load conditions. By adjusting the extraction radius under asymmetric load conditions, not only can the extraction efficiency be improved by approximately 3%, but it can also ensure that the extraction standards are met within 180 days, thereby effectively improving the overall performance of gas extraction.

Analysis of thermal runaway characteristics of lithium-ion batteries under high and low temperature short-term shocks

WANG Shaojia, WANG Zhi, ZHAO Qingjie, SHI Bobo

2024, 34(12):  159-167.  doi:10.16265/j.cnki.issn1003-3033.2024.12.1774

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In order to investigate the thermal runaway characteristics of lithium-ion batteries following short-term exposure to high or low temperatures shocks during transport and usage, thermal runaway tests were conducted on fully charged lithium-ion batteries. These batteries were subjected to temperature shocks at -40 to 60 ℃ for 24, 48, and 96 hours before external heating was applied. Analyses were performed on thermal runaway characteristic parameters to explore change rules in thermal runaway characteristics after short-term high- or low-temperature shocks. The results indicate that the overall severity of thermal runaway and the intensity of ejected flames were attenuated by the shock factor. With rising shock temperature, the time difference (Δt₁) between the onset of thermal runaway and the rupture of the battery safety valve shows slight fluctuations overall, while both the time difference (Δt₂) between peak temperature and the onset of thermal runaway and the initial thermal runaway temperature tends to increase. In contrast, peak thermal runaway temperature and flame heat flux decrease relatively. When the impact temperature decreases, Δt₁ gradually shortens, while Δt₂ initially increases and then decreases with lower temperatures. Both the onset and peak temperatures of thermal runaway drop significantly, with reductions of 15.2 and 175.4 ℃, respectively, observed at -40 ℃, along with a reduction in flame heat flux. Additionally, with extended shock durations, Δt₁, initial and peak temperatures of thermal runaway, and flame heat flux all decrease.

Public safety

Tourism safety risk assessment in mountainous scenic areas: a case study of Beijing

LIU Wei, DAI Ping, ZHAO Fanglei, CUI Kexin

2024, 34(12):  168-177.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0514

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To eliminate the impact of complexity and uncertainty of safety risks in mountainous scenic areas on operational safety, a risk assessment method for mountainous scenic areas was proposed. Firstly, risk factors in mountainous scenic areas were identified to develop a risk assessment index system including personnel, equipment and facilities, environment, and management. Then, FBN and AHP models were proposed to evaluate risk probabilities and losses. Moreover, an improved ALARP criterion was used to analyze the comprehensive safety risk of mountainous scenic areas. Finally, the performance and effectiveness of the risk assessment method were validated against safety risk assessment in mountainous scenic areas in Beijing. The results indicated that the BN-based risk assessment method for mountainous scenic areas effectively addressed the issue of complex risk factors and interdependent relationships between each level. The combination of BN and triangular fuzzy number can make full use of expert experience and avoid the subjectivity of expert opinions to a certain extent. The key risk factors in mountainous scenic areas were inadequate detection of dangerous amusement facilities, insufficient configuration or arrangement of forest fire prevention facilities, inadequate protective fencing for hazardous amusement projects, and rockfalls and landslides.

Urban spatially mixed traffic participants detection model based on improved YOLOv8n

ZHOU Junchao, CHEN Xin, GAO Jianjie, ZHANG Jie

2024, 34(12):  178-186.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0465

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In order to improve the recognition accuracy and detection speed of traffic participants by intelligent networked vehicles and traffic monitoring systems so that they can timely respond to the safety hazards in the mixed traffic environment in urban space, a mixed traffic participant detection model in urban space based on the improved YOLOv8n algorithm was proposed. Firstly, geometric transformation and pixel transformation enhancement strategies were employed in the data input stage to prevent overfitting and improve robustness, and generalization. Secondly, the SPD-Conv module was used to replace all original convolution layers of the YOLOv8n algorithm, which enhances the feature extraction capability for low-resolution small targets. Meanwhile, the CA module was added to the fusion structure of the neck network of the YOLOv8n algorithm to improve the recognition accuracy of key information with almost no additional computational overhead. Then, the boundary box loss function EIoU was used to replace the original loss function, enabling the model to achieve superior convergence speed and recognition stability. Finally, the ablation and comparison experiments were carried out with the public and self-built integrated traffic participant dataset, and the real-time detection experiment was carried out with the automatic driving experiment platform. The experimental results show that compared to the YOLOv8n model, the improved SEC-YOLO model has increased mAP and FPS by 3.2% and 7.9% respectively. The SEC-YOLO model outperforms mainstream models in terms of mAP and FPS as well. The average accuracy of real-scene detection on the automatic driving experimental platform is around 95%. The SEC-YOLO algorithm model achieves higher detection accuracy for urban traffic participants, with stronger robustness and real-time performance.

Lateral offset characteristics from main road to underground merging area in multi-entry underpass tunnel

JIAO Fangtong, SHI Zhenwei, DU Zhigang, GUO Peipei, FAN Dongkai, SUN Feng

2024, 34(12):  187-194.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0734

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The construction speed and number of vehicles passing through multi-portal underpass tunnels are increasing, and special driving environments such as long downhill slopes at the portals and underground merging areas cause frequent traffic accidents. To deeply analyze the variation characteristics of lateral offset in tunnel special sections, a test vehicle equipped with inertial navigation and Mobileye was used to perform a real-car natural driving test in a typical multi-entry underpass tunnel. Based on the tunnel's alignment and spatial variation characteristics, the test section was divided into the external tunnel section, the downhill section, the internal section, the underground merging section, and the internal tunnel section. The results indicated that the vehicle trajectories in the other four sections were more complex than the external section of the tunnel, and the lateral offset increased significantly, reaching up to 1.888 to 2.184 times the external section of the tunnel. The lane offset variation rate of the underground merging section and the inner section of the entrance was the smallest. The land discreteness of the underground merging section in the tunnel was the largest, and the discreteness of the entrance downhill section was the smallest with a standard deviation of only 0.111. The average width of the predicted interval of the entrance downhill and underground merging sections were 0.2 m and 0.24 m, respectively. Therefore, the driving safety hazards of the entrance downhill and underground merging sections were higher than other sections.

Impact of median strip facilities on spatial right of way of roads under shy away effects

CHEN Ying, DU Zhigang

2024, 34(12):  195-202.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0209

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In order to seek the best way of laying median strip facilities, four typical forms of median strips in China were established through driving simulation, and trajectory and speed-related data were extracted. Based on the horizontal right of way in the road spatial right of way, the road utilisation rate was selected as an indicator to analyse the difference characteristics between the actual right of way and the nominal right of way. The results show that the overall range of fluctuations in driver trajectories at different height facilities is significantly different. Among the rightward offsets triggered by the shy away effect, the trajectory offset reaches the maximum in the reboundable traffic cylinders scenario R₅, followed by the traffic separate railings R₇ and raised pavement markers R₂, which are larger than that of the double yellow line scenario R₁ without the facility. While adding facilities triggers a shy away effect in drivers, a certain degree of lateral offset can improve road utilisation rate, up to 11.15%, which was found in R₅. At the same time, the installation of the facility inhibits the speed of drivers, achieving a two-way improvement in traffic safety and traffic design. Finally, the width of the median strip to satisfy the maximum nominal right-of-way utilisation was calculated to be 0.844 m and the facility's height to be 138.62 cm.

Emergency technology and management

Analysis of emergency response to cabin turbulence based on dynamic Bayesian network

WU Yu, WU Xinyi, XIE Jiang

2024, 34(12):  203-212.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0411

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In order to effectively reduce the casualties and property losses caused by cabin turbulence, a decision analysis method based on a dynamic Bayesian network is proposed for cabin turbulence emergency response. Firstly, according to the relevant laws and regulations at domestic and international, combined with the emergency duties of key personnel on the ground and in the air, the turbulence emergency disposal process is analysed from pre-flight, in-flight and post-flight, and 24 key events are selected to construct a structured BT model. Secondly, the mapping conditions and transformation rules are established to form a DBN model. Then, the objective direct node a priori probability and the supplementary node fuzzy probability obtained by the triangular fuzzy probabilities of supplementary nodes obtained by the fuzzy number expert judgement method to obtain the a priori probabilities of all nodes. Finally, the time slice intervals of 1 and 2 min are selected to focus on the simulation inference of moderate and heavy turbulence, and to study the characteristics of the influence of each dynamic element on the failure of cabin turbulence event disposal. The results show that: the emergency response nodes are significantly affected by the degree of turbulence and time changes, and the optimal time for emergency response is within 5 min. Among them, the probability of failure for the failure of the crew fixation measures in place increases with the increase of the degree of turbulence, human factors such as the failure of the crew to fasten the seat belts and the over-servicing by the cabin crew are the key reasons for the failure of the response.

Knowledge-prompted few-shot relation extraction for emergency plan texts

ZHANG Kai, CHEN Qiang, NI Kai, ZHANG Yujin

2024, 34(12):  213-222.  doi:10.16265/j.cnki.issn1003-3033.2024.12.0308

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In order to accurately and quickly achieve relation extraction from few-shot emergency plan texts, KMKP based on knowledge prompts was proposed. First, a prompt template was constructed, utilizing learnable typed entity markers that incorporate relation semantics. The effectiveness of input guidance on the pre-trained language model (PLM) was thereby enhanced by these markers. Second, the boundary loss function was utilized to optimize model training, enabling the PLM to learn specific dependency relationships in the emergency domain and apply structured constraints to [MASK] predictions. Third, a gradient-free emergency knowledge storage database was created using the training data, and a knowledge retrieval mechanism was constructed by integrating KNN algorithm. The feature connections between training and prediction data can be captured through this mechanism and the gradient-free normation was used to correct the predictions of PLM. Finally, the experimental validation and analysis were performed using four public datasets under few-shot settings (1-, 8-, and 16-shot). The results show that compared to the state-of-the-art model, KnowPrompt, F1 score is boosted by an average of 2.1%, 2.8%, and 1.9% by KMKP. In a 16-shot emergency plan instance test, a relation extraction accuracy of 91.02% is achieved by KMKP. Catastrophic forgetting and overfitting issues in few-shot scenarios are effectively mitigated.