CRISP Type 2: Interdependent Network-based Quantification of Infrastructure Resilience (INQUIRE)

关键基础设施系统的高效运行越来越依赖于彼此。这项研究将发展一种定量的、预测的网络弹性理论，考虑到已建成的基础设施网络与使用它们的人类和社区之间的相互作用。该框架有可能指导城市官员、公用事业运营商和公共机构制定基础设施管理和城市规划的新战略。更一般地说，这些努力将解开网络结构和网络动力学的角色，使相互依赖的系统能够承受、恢复和适应扰动。这项研究将会引起从生态学到细胞生物学等许多其他领域的兴趣。该项目将首先将三个已构建的基础设施和已知的相互依赖关系（物理和功能）编目到一个适合建模的“网络的网络”表示中。这项研究的一个关键部分还在于量化已建成的基础设施与社会系统之间的相互作用。因此，这些模型将通过城市“经济计量学”（基于调查的经验数据，捕捉市民和社区如何利用城市服务并在紧急情况下做出反应），将社区层面的行为影响纳入其中。这种对基础设施及其相互依赖性的现实核算，将辅之以对其未来可能面临的危险的现实估计。研究的核心将使用基于网络的分析和计算方法来识别相互依赖的基础设施（高维）动态状态的降维表示。检查这些弹性指标在组件级别的网络压力下如何变化（例如，由飓风后的洪水引起的），将允许识别现有相互依赖的基础设施中的弱点。相反的情况——对网络的刻意改变可能会提高弹性或加速已经失败的系统的恢复——也将被探索。

Critical infrastructure systems are increasingly reliant on one another for their efficient operation. This research will develop a quantitative, predictive theory of network resilience that takes into account the interactions between built infrastructure networks, and the humans and neighborhoods that use them. This framework has the potential to guide city officials, utility operators, and public agencies in developing new strategies for infrastructure management and urban planning. More generally, these efforts will untangle the roles of network structure and network dynamics that enable interdependent systems to withstand, recover from, and adapt to perturbations. This research will be of interest to a variety of other fields, from ecology to cellular biology.The project will begin by cataloging three built infrastructures and known interdependencies (both physical and functional) into a "network of networks" representation suitable for modeling. A key part of this research lies in also quantifying the interplay between built infrastructure and social systems. As such, the models will incorporate community-level behavioral effects through urban "ecometrics" -- survey-based empirical data that capture how citizens and neighborhoods utilize city services and respond during emergencies. This realistic accounting of infrastructure and its interdependencies will be complemented by realistic estimates of future hazards that it may face. The core of the research will use network-based analytical and computational approaches to identify reduced-dimensional representations of the (high-dimensional) dynamical state of interdependent infrastructure. Examining how these resilience metrics change under stress to networks at the component level (e.g. as induced by inundation following a hurricane) will allow identification of weak points in existing interdependent infrastructure. The converse scenario--in which deliberate alterations to a network might improve resilience or hasten recovery of already-failed systems--will also be explored.