安全科学与韧性(英文)最新文献

Evidence suggests that in the event of a fire accident, a certain number of building occupants escape through smoke-filled environments. Consequently, evaluating the corresponding evacuation performance under such life-threatening conditions is important for advancing fire safety analyses. This study aimed to develop a fire-integrated evacuation model to consider the effects of spreading fire hazards (i.e., radiation, temperature, toxic gas, visibility) on evacuees in a room fire evacuation scenario. Furthermore, a novel quantitative approach was introduced to evaluate evacuees’ local fire risks and stress levels according to their egress paths. The escape characteristics at various stages of fire development were studied as well. The results demonstrate that evacuation performance varies considerably depending on the severity of evacuees’ confronted fire hazard conditions, which emphasizes the importance of minimizing the pre-evacuation time in fire evacuation emergencies.

To solve the problem of volunteer dispatch during the Coronavirus Disease 2019 (COVID-19) epidemic, a many-to-many two-sided matching volunteer dispatch method based on an improved predator-search algorithm is proposed. First, different evaluation index sets for volunteers and rescue tasks were developed, and weightings were determined using the analytic hierarchy process. Subsequently, the actual and expected values of the different indicators of the two parties were determined, and the triangular fuzzy number was used to calculate the satisfaction of the two parties. Based on this number, we used a linear weighting method to calculate the combined satisfaction and build a many-to-many two-sided matching model according to the demands of both parties. Subsequently, an improved predator-search algorithm was used to solve the model. Finally, taking the recruitment of volunteers for pneumonia epidemic prevention and control in Chun'an County as an example, the method proposed in our study was verified. A comparison and analysis of the results further demonstrated the feasibility and advantages of this method.

Herein, a matching method for varied epidemic phase characteristics and appropriate strategies is presented, aiming at the complexities of the novel coronavirus disease 2019 pandemic and the hysteresis of strategies. The classical diffusion dynamic models of the epidemic spread are compared. Furthermore, the Bass diffusion model is selected to study more characteristics of an epidemic, such as the diversities in regional internal and external infection rates. Thereafter, the classical division method vis-à-vis a pandemic life cycle is improved. A specific approach is proposed to portray more detailed characteristics of the pandemic by dividing it into more phases. Next, a four-level epidemic prevention and control measure system is concretized. The applicable phases and strategic effectiveness of each measure are integrated into the different phases of the pandemic life cycle. Finally, the matching method is applied to analyze two cases of the spread of the outbreak in cities and significant events. The findings provide a certain reference for the effectiveness of the matching method in the post-peak epidemic period.

In recent years, urban resilience has attracted increasing attention from researchers and managers from the international community at the national, regional, and urban levels. Numerous multi-dimensional and cross-disciplinary investigations, campaigns, and outlines have significantly promoted the development goal of resilience in cities worldwide. However, the existing definitions and interpretations of urban resilience still call for a more comprehensive, systematic, and exhaustive analysis as urbanization accelerates and the complex risks of various safety events increase. To this end, we rethink the extension and connotation of urban resilience based on a review and analysis of critical hotspots, realistic demand, and development trends. A conceptual classification with three aspects and three typical tiers of urban resilience is proposed, which further promotes a new definition and interpretation by incorporating the resilience extension of urban systems. In addition, the six-dimensional characteristics are extracted to furnish the urban resilience connotation, and four-stage improvement measures are introduced accordingly. In addition, the newly developed urban resilience is applied to a case analysis of a large-scale disaster, which demonstrates the necessity and significance of this study. The new extension and connotation investigation will be helpful for the improvement and implementation of urban resilience, thereby guiding the construction of resilient cities.

Unreinforced masonry (URM) made with soft bricks comprises a large percentage of the building stock in developing countries. However, the poor performance of URM piers during earthquakes has led to renewed interest in understanding their behavior under lateral loads. Little experimental data is available on the seismic response, analysis, and design of URMs made of soft bricks. In this study, the micro-modeling technique is used to simulate the in-plane behavior of load-bearing, soft-brick URM piers. The parameters required in the constitutive models are obtained from material tests and used to develop a calibrated numerical model of the URM piers. Piers with various aspect ratios subjected to various axial stresses are numerically modeled to obtain monotonic and cyclic responses, and their critical displacement limit states are identified. Changes in the failure modes of masonry piers with variations in the aspect ratio and axial stress are established. Load-bearing piers exhibit three distinct failure modes: bed sliding, diagonal shear cracking, and flexure, depending on the aspect ratio and axial stress. The seismic fragility of each pier failure type is examined using nonlinear time history analyses. The results show that bed-sliding piers collapse at extremely low PGA levels. Piers failing through diagonal shear cracking also fail at low PGA levels. Flexural piers can resist seismic forces up to a slightly higher PGA level and thus are the last to collapse. The results also indicate that the effect of uncertainty in ground motions is more significant than the effect of variability in the masonry pier capacities.

In recent decades, numerous catastrophic accidents have occurred worldwide, ranging from natural events (such as tsunamis, flooding, and earthquakes) to industrial events (such as mining and chemical process disasters). They endanger humans, the environment, organizations, and societies, as well as national security. However, there have been few attempts to propose a model for assessing the required capabilities and potential challenges in crisis management system maturity (CMSM). Accordingly, this study proposes a framework for measuring CMSM levels in complex systems. To this end, a CMSM taxonomy was developed, including aspects, dimensions, and factors influencing the CMSM. Fuzzy inference sets and fuzzy analytical hierarchy processes are then used for knowledge acquisition, quantification of the CMSM, and dealing with epistemic uncertainty. An actual complex petrochemical plant is investigated to evaluate its capabilities. The findings revealed the most significant contributing factors: the CMSM’s current capability and challenges level (score), as well as the capacity to overcome the potential crisis response challenges. Moreover, the proposed model can be used as a practical approach for various chemical-processing plants.

Urban infrastructures are invariably constituted by social and technical components whose capacity to withstand crisis is determined by the resilience of their sociotechnical structures. This study aims to apply the principles of sociotechnical resilience in modeling and simulating disaster response in urban areas. Drawing on a case study of Jakarta, Indonesia, our study focuses on the role of hospitals as part of healthcare infrastructure in response to a large-scale disaster. Each hospital is modeled as a coordinated location with a certain amount of resources, primarily in terms of medical staff. We perform sensitivity analysis through Monte Carlo simulations to observe the impacts of various response strategies, disaster severity, and communication duration on system resilience. The results show that centralized systems are generally more suitable for dealing with low disaster severity, while the decentralized strategy performs better during a disaster with worse impacts. Additionally, the time taken for communication and coordination can significantly affect the performance of centralized systems. By simulating various scenarios, parameters, and recovery protocols, the model we developed can help policymakers, city planners, and other stakeholders design proper response strategies suitable to their structural conditions and available resources during a large-scale disaster in urban cities.

Accidents are common in the petroleum industry. The risk of accidents can be easily minimized by understanding the harm early in the production system. This study presents a perception-based risk and safety analysis of petroleum production systems. Data were collected from three fields operated by Sylhet Gas Fields Limited in Bangladesh. The Statistical Package for the Social Sciences (SPSS) software was used to analyze the data. The results were then subjected to a frequency analysis, an analysis of variance (ANOVA), and a reliability analysis. The frequency analysis indicated the overall safety situation, and the ANOVA models and reliability analysis substantiated the results. A chi-squared test indicated the association between the datasets. The outcomes of the risk matrix indicated various risk levels, such as low, moderate, and high. According to the implicit risks, necessary measures were recommended for the industry's future.

The COVID-19 pandemic is strongly affecting many aspects of human life and society around the world. To investigate whether this pandemic also influences crime, the differences in crime incidents numbers before and during the pandemic in four large cities (namely Washington DC, Chicago, New York City and Los Angeles) are investigated. Moreover, the Granger causal relationships between crime incident numbers and new cases of COVID-19 are also examined. Based on that, new cases of COVID-19 with significant Granger causal correlations are used to improve the crime prediction performance. The results show that crime is generally impacted by the COVID-19 pandemic, but it varies in different cities and with different crime types. Most types of crimes have seen fewer incidents numbers during the pandemic than before. Several Granger causal correlations are found between the COVID-19 cases and crime incidents in these cities. More specifically, crime incidents numbers of theft in Washington DC, Chicago and New York City, fraud in Washington DC and Los Angeles, assault in Chicago and New York City, and robbery in Los Angeles and New York City, are significantly Granger caused by the new case of COVID-19. These results may be partially explained by the Routine Activity theory and Opportunity theory that people may prefer to stay at home to avoid being infected with COVID-19 during the pandemic, giving fewer chances for crimes. In addition, involving new cases of COVID-19 as a variable can slightly improve the performance of crime prediction in terms of some specific types of crime. This study is expected to obtain deeper insights into the relationships between the pandemic and crime in different cities, and to provide new attempts for crime prediction during the pandemic.

Modern society is confronted with emerging threats from chemical, biological, and radiological (CBR) hazardous substances, which are intensively utilized in the chemical, medical, and energy industries. The atmospheric dispersion of released CBR hazardous pollutants can influence a large percentage of the population owing to their rapid process with extensive spatial coverage. It is important to comprehensively understand the behaviors of the released CBR pollutants in the atmosphere to fully evaluate the risks and protect public safety. In this study, we reviewed the advancements in the atmospheric transport of CBR pollutants, including the urban atmospheric boundary layer, unique concepts, and models for CBR pollutants. We underlined the development of innovative methodologies (e.g., inverse estimation and data assimilation methods) for the atmospheric transport of accidentally released CBR pollutants to reduce uncertainties in emissions and accumulated errors during dispersion by combining numerical models with monitoring data. Finally, we introduced progress in quantitative risk assessment, including exposure assessment and dose-response relationships for CBR hazardous pollutants. A framework, source, assimilation, fundamentals, exposure, and risk (SAFER), has been proposed to integrate the key components in the risk assessment of airborne CBR hazardous pollutants. These methods and models can contribute to effective risk preparedness, prevention, evidence-based policymaking, and emergency response to airborne CBR pollutants.