Interdependent infrastructure systems consist of several sectors, including gas, power, transportation, and health systems, and are important in modern societies because they provide essential services for the continuous functioning of communities and society. This study presents a framework for conducting vulnerability analyses and key sector identification in interdependent infrastructure systems during and after the occurrence of an external perturbation. To analyze the correlation of sectors and identify the key sectors at different stages of a hazard, this study introduces input-output models, demand-driven inoperability input-output models (IIMs), and supply-driven IIMs. Moreover, an interdependency matrix is used to construct the network in an interdependent infrastructure system, and new indicators of importance and vulnerability indexes are defined to analyze changes in key sectors in different periods of an external hazard. The proposed model is illustrated through a hypothetical four-sector interdependent infrastructure system based on a flash flood event. However, the model is applicable to any physically interdependent system, such as a transportation system, power supply system, and so on. The importance and vulnerability attributes of interdependent infrastructure systems at different stages of the risk management process also provide guidance for decision makers involved in dealing with natural hazards such as flash flood events. (C) 2021 American Society of Civil Engineers.

Global pandemics, such as the Coronavirus Disease 2019 (COVID-19), have serious harmful effects on people′s physical health and mental well-being. It is imperative therefore that we seek to understand community resilience and identify ways to enhance this, especially within our cities and communities. Therefore, great emphasis is now placed on how cities prepare for and recover from such disasters, and community resilience has emerged as a key consideration. Drawing upon research on the theory of resilience, this study seeks to identify the factors that influence community resilience and to analyze their causation toward helping to manage the risks associated with the COVID-19 pandemic. Seventeen factors from the five dimensions of social capital, economic capital, physical environment, demographic characteristics, and institutional factors are used to construct an index system. This is used to establish the structural level and importance of each factor. Data were collected using a questionnaire survey involving 12,000 members of key community groups in the city of Wuhan. An interpretative structural model (ISM) combining the analytic hierarchy process (AHP) method was then used to obtain the multi-level hierarchical structure composed of direct factors, indirect factors, and fundamental factors. The results show that the income level, vulnerability of the population, and the built environment are the main factors that affect the resilience of communities affected by COVID-19. These findings provide useful guidance toward the effective planning and design of urban construction and infrastructure. The results are expected to be useful to inform future decision-making and toward the long term, sustainable management of the risks posed by COVID-19.

While various measures of mitigation and adaptation to climate change have been taken in recent years, many have gradually reached a consensus that building community resilience is of great significance when responding to climate change, especially urban flooding. There has been a dearth of research on community resilience to urban floods, especially among transient communities, and therefore there is a need to conduct further empirical studies to improve our understanding, and to identify appropriate interventions. Thus, this work combines two existing resilience assessment frameworks to address these issues in three different types of transient community, namely an urban village, commercial housing, and apartments, all located in Wuhan, China. An analytic hierarchy process–back propagation neural network (AHP-BP) model was developed to estimate the community resilience within these three transient communities. The effects of changes in the prioritization of key resilience indicators under different environmental, economic, and social factors was analyzed across the three communities. The results demonstrate that the ranking of the indicators reflects the connection between disaster resilience and the evaluation units of diverse transient communities. These aspects show the differences in the disaster resilience of different types of transient communities. The proposed method can help decision makers in identifying the areas that are lagging behind, and those that need to be prioritized when allocating limited and/or stretched resources.

当前,各类突发事件的频发给城市带来了巨大冲击和损害,社区作为首先感知灾害风险的基础单位,建立和增强其灾害弹性对于提升城市社区应对危机的自组织、自适应和可持续发展能力具有重要意义.基于该问题,以城市灾害易发的流动人口社区作为评估对象,建立了适用于流动人口社区特点的弹性指标评价体系,并运用ISM法分析了16个指标间的具体影响关系.结合CRITIC-TOPSIS法客观评价,比较和分析了流动人口规模较大的15个城市的流动人口社区弹性,并对流动人口社区弹性与流动人口数量进行相关性分析.结果 表明,资源可获得性、经济水平及年龄结构是影响流动人口社区弹性的深层根本原因;各城市流动人口社区其弹性水平较低,且普遍存在住房条件差、社会保障不足、健康教育与宣传不到位等问题;流动人口数量与其社区弹性呈显著负相关关系.