安全科学与韧性(英文)最新文献

Grain security guarantees national security. China has many widely distributed grain depots to supervise grain storage security. However, this has led to a lack of regulatory capacity and manpower. Amid the development of reserve-level information technology, big data supervision of grain storage security should be improved. This study proposes big data research architecture and an analysis model for grain storage security; as an example, it illustrates the supervision of the grain loss problem in storage security. The statistical analysis model and the prediction and clustering-based model for grain loss supervision were used to mine abnormal data. A combination of feature extraction and feature selection reduction methods were chosen for dimensionality. A comparative analysis showed that the nonlinear prediction model performed better on the grain loss data set, with R² of 87.21%, 87.83%, 91.97%, and 89.40% for Gradient Boosting Regressor (GBR), Random Forest, Decision Tree, XGBoost regression on test sets, respectively. Nineteen abnormal data were filtered out by GBR combined with residuals as an example. The deep learning model had the best performance on the mean absolute error, with an R² of 85.14% on the test set and only one abnormal data identified. This is contrary to the original intention of finding as many anomalies as possible for supervisory purposes. Five classes were generated using principal component analysis dimensionality reduction combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, with 11 anomalous data points screened by adding the amount of normalized grain loss. Based on the existing grain information system, this paper provides a supervision model for grain storage that can help mine abnormal data. Unlike the current post-event supervision model, this study proposes a pre-event supervision model. This study provides a framework of ideas for subsequent scholarly research; the addition of big data technology will help improve efficient supervisory capacity in the field of grain supervision.

Computational combustion modeling was introduced in China in the early 1980s. Specifically, after the first computational fluid dynamics (CFD) lecture in China given by Brian Spalding, Professor of Heat Transfer and Head of the Computational Fluid Dynamics Unit at Imperial College, London, under the invitation from Professor Wei-Cheng Fan of University of Science and Technology of China, Anhui, China, in 1984. This paper presents the development history of computational combustion research led by Fan in China. During the early stages of CFD development in the 1980s and 1990s, numerous innovative models, methods, and computational tools were proposed and developed by Fan's research group to advance computational combustion research in China. Throughout the evolution of computational combustion technologies, the State Key Laboratory of Fire Science, founded and led by Fan, has played a leading role in the development and deployment of these technologies in China. Recent progress in low-pressure combustion and battery fire research and combustion modeling technologies are described.

Overcrowding and stampedes may occur in public places with the gathering of crowds. To mitigate and prevent risk, the accident mechanism and methods for monitoring and evaluating crowd-gathering risk were investigated. Related studies are reviewed and summarized in this paper. The evolution process of crowd-gathering risk and precipitating factors were explained systematically. Risk monitoring methods are classified into three types according to the key technologies adopted. Articles exploring risk evaluation methods for crowd gathering are outlined, and the three main paradigms were formed. Finally, the shortcomings and future research points are summarized to promote more in-depth and comprehensive studies on crowd-gathering risk, develop monitoring technologies, and build an integrated system of risk management.

This paper reviews the composition, development, and operation mechanism of domestic and foreign fire protection technical regulations and standards. Considering the development and problems of China's fire protection standard system and fire protection technical standards, this paper recommends fire safety professional engineering design specialization, suggests key research fields pertaining to fire protection technical standards, and elucidates how a performance-based fire protection technical standard system can be established.

Hot molten metal droplets with high fire hazard can easily ignite combustibles. Molten metal kinetics on the surface of a combustible material directly affects the ignition likelihood. Existing research focuses on the behavior of droplets during collisions, rarely addressing the ignition of combustibles by hot metal droplets. Here, the mechanisms of aluminum and copper droplets impinging on and igniting extended polystyrene (EPS) foam boards at different velocities were investigated. The relationship between the critical ignition temperature and impact velocity of droplets with diameters of 6 and 8 mm was experimentally studied for aluminum droplets; the critical ignition temperature non-monotonically depended on the impact velocity. For copper droplets, the relationship between the ignition probability and the impact velocity of droplets with diameters in the 3.5–7 mm range was experimentally studied. The most obvious difference between the two droplet ignition types was that the impact of copper droplets was accompanied by intense splashing, and the fragmentation extent positively correlated with the impact velocity. It was challenging to ignite using completely broken copper droplets. Droplets with the diameter of 5 mm were the most dangerous under the experimental conditions of this study, because the foam could still be ignited at higher impact velocities. Numerical simulations suggested that the main factors explaining the critical ignition temperature of aluminum droplets were gas mixing and splat cooling. The main factor affecting the ignition of copper droplets was fragmentation, and experimental observations were explained using non-dimensional droplet fragmentation theories.

Safety resilient city is a frontier concept of urban safety development and a hot topic in the field of urban safety research. In this paper, the relevant research results of domestic and foreign scholars are reviewed from the perspectives of concepts and models, the evaluation indicator system of urban safety resilience is compared in terms of risk types, evaluation objects, evaluation dimensions and quantitative methods, and the development of international standards for resilient cities is discussed. Based on the literature review, the connotation of the triangular theoretical model of urban safety resilience is explained, and an urban safety resilience evaluation index system applicable to Chinese cities is proposed, which provides support for the development of the national standard “Guide for safety resilient city evaluation” (GB/T 40947-2021). It is applied to six representative cities as examples for evaluation to explore the direction of Chinese urban safety resilience improvement. The pathway for improving the safety resilience of Chinese cities is discussed.

To assist the Department of Emergency Management in understanding the overall risk characteristics and situation of an urban agglomeration for a reasonable risk prevention and control strategy, this study developed a comprehensive multi-hazard risk assessment model for an urban agglomeration with multiple factors. The proposed model includes disaster probability and disaster loss sub-models. The model evaluated four types of disaster risk in urban agglomerations: natural disasters, accidental disasters, public health incidents, and social security incidents. In addition, a variety of factors were integrated into the model, including the socioeconomic foundation of urban agglomerations, the oligopoly effect of core cities, historical disaster losses, the effect of disaster chains, the ability of disaster prevention and mitigation, and intercity coordinated rescue capabilities. Finally, the risk assessment model was applied to the Beijing-Tianjin-Hebei urban agglomeration. The assessment results were compared to the distribution of the new coronavirus pneumonia epidemic in the target urban agglomeration. The results showed that after analyzing the risk characteristics and evaluating the risk levels, the model not only showed the comprehensive risk levels and distribution of urban agglomerations but also revealed the high-risk areas and the key points of risk prevention and control. More importantly, the results obtained through the model can facilitate the strategic planning of disaster prevention and mitigation for urban agglomerations.

This study revisits the concept of resilience by critically reviewing the contents of previous literature. Furthermore, it explains a new methodology for measuring resilience based on the theory of springs and qualitatively appraises the resiliency of Minamisanriku town as a case study. Minamisanriku is a tiny coastal town located in the northeastern part of the Miyagi Prefecture, Japan. The town was affected by an earthquake on March 11, 2011, with a magnitude of 9.0, followed by a tsunami. According to the authors’ previously proposed conceptual framework, resilience should be considered by dividing it into three components: onsite capacity, instantaneous survivability, and the recovery potentiality of an area. Each component of the framework depends on two or three factors that can be measured using different indicators and sub-indicators. Onsite capacity is the ability of a given place to withstand a tsunami before it arrives, and it has been considered indispensable for the prevention of a tsunami. Instantaneous survivability is the power to be alive at the point of a disaster climax. Returning speed to its normal daily routines once a catastrophe is over is called recovery potentiality. It is understood that strengthening onsite capacity by moving residences to higher ground, building seawalls and paved roads, relocation of fishing industry infrastructure, and land elevation in Minamisanriku town makes it a benchmark for resilient cities.

Grouping is a common phenomenon that occurs everywhere. The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics. However, a general method is lacking to quantitatively explain leadership in an evacuating group. To understand the evolution of single-group dynamics throughout an evacuation, we developed an extended social force model integrated with a group force. A series of single-group evacuations from a room were simulated. An information-theoretic method, transfer entropy (TE), was applied to detect predefined and undeclared leadership among evacuees. The results showed that the predefined leader was correctly detected by TE, suggesting its capability in measuring leadership based on time series of evacuees’ movement information (e.g., velocity and acceleration). When evacuees were grouped together, TE was higher than when they were alone. Leaders presented a monotonically increasing cumulative influence curve over the investigated period, whereas followers showed a diminishing tendency. We found that leadership emergence correlated with evacuees’ spatial positions. The individual located in the foremost part of the group was most likely to become a leader of those in the rear, which concurred with the experimental observations. We observed how a large group split into smaller ones with undeclared leadership during evacuation. These observations were quantitatively verified by TE results. This study provides novel insights into quantifying leadership and understanding single-group dynamics during evacuations.

Exit choice is essential for pedestrian safety and evacuation efficiency during the context of an emergency. Cumulative prospect theory is a widespread realistic decision-making theory that can transform choice outcomes and probabilities into subjective terms and integrate them as a parameter of cumulative prospect value (CPV), which determines the decision. The main contribution of this paper is the use of the CPV to predict exit choice. Furthermore, the different decision-making rules including Max, exponential(Expo), and Ratio (i.e., the three variable choice functions) were summarized and examined. This study used a confusion matrix to compare the predicted results with experimental data. Consequently, the parameters of Accuracy and F1-score showed that the predictions from Max and Expo were significantly more realistic while the results from Ratio were much more robust.