To enhance the identification and control of accident risks in confined space operations in industrial and commercial enterprises, a socially acceptable risk standard for such operation accidents was established, and a risk grading study based on F-N curve was conducted. First, historical data on significant and higher-level accidents in confined space operations from 2014 to 2021 were analyzed statistically in terms of fatalities, accident frequency, and the number of enterprises involved, in order to identify accident patterns. Then, considering accident frequency and fatality consequences, a socially acceptable risk standard was developed based on ALARP criteria, and F-N curve was plotted. Finally, a risk grading management model was established based on three risk zones-intolerable, acceptable, and negligible. The control measures were proposed from both enterprise and individual perspectives. The results show that for accidents with 3 to 10 fatalities, the frequency falls within the magnitude of 10^-6-10^-4. The F-N curve with a slope of -4.033 is used to divide three distinct risk levels, helping to clarify the boundaries of risk levels and highlight management priorities.

In order to investigate the performance of the latest Chinese Planned Controlled Access Highway Network (CAH-NET), A modeling method in Space-P was proposed, a composite importance index for nodes was introduced, and a vulnerability analysis approach based on node failures guided by this index was presented in this study. Based on the data from the 2022 National Highway Network Planning, the CAH-NET model was constructed, and its static topological characteristics as well as network vulnerability under various attack strategies were analyzed. Results show that the current CAH-NET exhibits typical small-world and scale-free properties, with good overall accessibility. The top rankings for node degree, centrality, and composite importance are mostly occupied by cities in the eastern coastal areas or key central hub cities. The vulnerability of network is slightly affected by random attacks. About 90% of nodes t need to be attacked to completely destroy the CAH-NET. Among intentional attack strategies, the connectivity of network declines most quickly. When the proportion of nodes attacked through the betweenness centrality attack strategy reaches about 30%, the network totally collapses.

To investigate the influence mechanisms of smoke environments on pedestrian's evacuation route choice behavior during fires and toxic gas leakages, and to overcome the research challenges posed by ethical constraints that make real-life human experiments difficult to conduct, in this paper, a virtual evacuation experiment platform was constructed. In order to study the influence of smoke level and route length on evacuees' route choice behavior, a single-player route choice experiment was designed and carried out under the influence of smoke, taking into account the pedestrian's speed difference under different smoke scenes in real life. Results indicate that both smoke level and route length have significant influence on evacuees' evacuation route choice behavior. The evacuees prefer to choose the route with lower smoke levels and shorter length. The results of the binary logistic regression model show that compared with the route length (Odds Ratio, OR=15.516), the smoke level (OR=45.475) has a greater influence on the route choice of evacuees. However, a small number of evacuees took a risk, that is, choosing a route with higher smoke levels for evacuation. The post-experiment questionnaire results demonstrated that participants could experience a reasonably authentic evacuation process in smoke-filled environments within the virtual reality scenarios, indicating high levels of validity and reliability for the study.

To accurately identify deep foundation pit collapse risk levels and improve construction safety, a risk assessment model was developed using variable fuzzy set theory. Sixteen influencing factors were screened from four categories: hydrogeology, support conditions, construction operations, and management monitoring. A risk index system was constructed based on these factors with clearly defined grade standards for risk classification. The multiplicative synthesis method was applied to optimally combine subjective weights derived from Stepwise Weight Assessment Ratio Analysis (SWARA) and objective weights calculated via the entropy weight method, forming comprehensive weights that integrate expert judgment and data objectivity. Variable fuzzy set theory was then utilized to process sample data, generating comprehensive grade characteristic values to determine collapse risk levels. Engineering case results show that the evaluation outcomes of method align with actual construction conditions, confirming its scientific validity and effectiveness. Compared to existing approaches, it more accurately reflects risk status by addressing the fuzzy nature of risk boundaries in deep foundation pit projects. This study provides a scientific and practical framework for risk assessment, enhancing safety management in deep foundation pit construction and offering practical value for ensuring project safety and informed risk management decisions.

To identify the primary types and distribution patterns of risky driving behaviors at the entrance and exit of the highway tunnel, a risk driving behavior spectrum was constructed based on trajectory data. Firstly, trajectory data were collected through continuous video surveillance of Liupan Mountain Tunnel in Ningxia. Vehicle traffic characteristics at tunnel entrance and exit were analyzed and four categories of risky driving behaviors were selected, including rapid speed change, serpentine driving, dangerous following, and dangerous lane changing. Then, the measurement of risk method was applied to quantify four types of risky driving behaviors, and quartile deviation and criteria importance though intercriteria correlation (CRITIC) were used to determine the threshold values of risky driving behavior characteristics and weights, and the characteristic values of drivers' risky driving behavior spectrum were calculated. Finally, driving behavior scores were compared with characteristic thresholds, and the spatial distribution of hazard points for typical risky behaviors of dangerous drivers was statistically analyzed. The results of the study show that high-risk drivers in the entrance/exit sections of the tunnel mainly show the behaviors of rapid speed change or serpentine driving, in which the rapid speed change behavior has the most significant effect on the total score of the risky behavioral spectrum of the drivers. Vehicles have large speed variations at both the inside the tunnel entrance and outside the exit from 0 to 50 meters. Moreover, larger speed changes are observed at the exit section (0-50 m) compared to the entrance section, and risk points for rapid speed change behavior of dangerous drivers are also more concentrated in that segment. The lateral offset values outside the tunnel entrance/exit are larger than those inside the tunnel, and the risk points for serpentine driving behavior of dangerous drivers are mainly distributed in the range of 0-50 meters outside the tunnel entrance and exit. Risky driving behavior spectrum based on trajectory data can assess and quantify the risk level of drivers in the tunnel entrance/exit sections, facilitating precise identification of high-risk drivers.

In order to comprehensively assess the risk of urban UAVs falling onto ground transportation, a systematic risk assessment method combining theoretical derivation and simulation experiments was proposed. Firstly, the urban air-ground traffic network structure was constructed. Considering the dynamics of UAVs and ground vehicles, as well as the traffic flow characteristics of the air-ground traffic network, theoretical derivation was used to determine the possible ground traffic risk interval. Secondly, Monte Carlo simulation was carried out to obtain the ground traffic risk value based on the statistical results of the test. Then, the theoretical analysis and simulation test analysis were combined to comprehensively evaluate the risk of UAVs falling to ground traffic. Finally, a simulation case study was carried out in Tianjin. The results show that the risk of UAVs falling on ground traffic is affected by several factors such as crash probability, ground traffic density, and the structure of the air road network. The statistical risk value of the simulation test is consistent with the theoretical analysis of the risk interval, which preliminarily verifies the reasonableness of the proposed method.

In order to optimize the airport security screening channel, the comparison of traditional security channel equipment configuration and new security channel equipment configuration, the cost of the two types of channels was analyzed under the requirements of saving security resources and security. The response value of security screening equipment was simulated by normal distribution, and the false alarm rate and false clear rate were controlled by equipment threshold. The binary decision tree model was used to characterize two types of error probability formulas. For the two types of channel equipment configuration, a nonlinear programming model was established to minimize the security screening cost with the probability of two types of errors as the constraint condition, and the Monte Carlo simulation algorithm was used to solve the problem. The results show that when the new channel is not sampled, the security screening cost of using the new channel can be saved by about 24% compared with the traditional method. When the new channel is sampled at the traditional channel ratio, the cost of using the new channel security screening can be saved by about 18% compared with the traditional one. When the average number of daily single-channel passengers N₁ is between 1 000 and 2 000, the traditional security screening channel equipment can be replaced by a new type of security screening channel equipment, as the per capita security cost is lower than that of the traditional channel. when N₁ is between 500 and 1000, considering the previous fixed cost input problem, the use of traditional channel equipment can be maintained.

In order to solve the problem of low guidance efficiency of emergency evacuation signs, a layout optimization method of emergency evacuation signs considering pedestrian perceptual behavior was proposed. Firstly, the recognizable range and probability distribution of the signs were investigated to establish a model of pedestrians' perceptual behavior towards the signs; secondly, a layout optimization model was constructed considering the number, location and orientation of the signs by combining it with the distribution of the guidance demand in the multi-exit space; finally, a case study of a cruise ship was used, where a genetic algorithm is applied to solve the problem. The evacuation efficiency, both before and after the optimization of the signage layout, was evaluated through microscopic crowd evacuation simulation, respectively. The results show that the average evacuation time under the optimized layout is 10.3% shorter than that under the unoptimized layout when the pedestrians are randomly distributed at the initial position; the average evacuation time is 4.2% shorter under the scenario simulating the actual evacuation process, and the number of pedestrians guided to different destinations is more balanced.

In order to systematically analyze the behavioral safety risks of contractors in oil and gas pipeline projects and improve the scientificity and standardization of risk assessment work, a contractor behavioral safety risk assessment model was constructed based on the concept of full life cycle management, integrating the three key elements of the safety management system, behavior and status. The weights of indicators at all levels were determined by the analytic hierarchy process (AHP), and a quantitative scoring mechanism was established. The model was verified by cases such as the Yixian sub-transmission station, the Beijing branch water conservation project, and the Linxian operation area sewage treatment system replacement project. The results show that typical problems in the stages of business selection and pre-entry preparation, project construction, project end and completion include incomplete construction plans, unclear personnel responsibilities, missing safety supervision records, and low training coverage. The assessment model can identify and classify different types of risks and clarify their distribution characteristics throughout the life cycle. The behavioral safety risks of oil and gas pipeline contractors have different manifestations at different stages. There is a significant correlation between organizational behavior and management status, and risks are prone to concentrate on the weak links of stage connection and management chain.

To address the challenges of high risk and difficulty in quantitative analyzing hazardous chemical storage tank area leakage accidents, a composite method based on STAMP was proposed to elucidate the accident mechanisms. It clarifies the logical relationships among causal factors and quantitatively evaluate the impacts of accident causation, thereby enabling nonlinear quantitative accident analysis. First, the STAMP-24Model was utilized to construct an accident analysis diagram for hazardous chemical storage tank leakage, identifying system components, hierarchical relationships, as well as analyzing accident causal factors and their logical connections. Subsequently, Interpretive Structural Modeling (ISM) method was applied to determine path relationships and hierarchical structures among causal factors. Node importance analysis based on degree and clustering coefficients, as well as BN node analysis, was conducted to assess the criticality of causal factors on system. Finally, the validity and feasibility of the method were verified through a case study. The results show that organizational management failure (e.g., failure to implement safety rules and regulations, lax implementation of engineering management regulations) is the core driver of accident risk evolution, with a total degree value of 53.3%, and a significant coupling effect with physical and personnel layer factors.