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。这项研究将开发一种量化的、可预测的网络弹性理论，该理论考虑到已建成的基础设施网络与使用它们的人和社区之间的相互作用。这一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。更广泛地说，这些努力将理清网络结构和网络动态的作用，使相互依赖的系统能够承受、恢复和适应扰动。这项研究将对从生态学到细胞生物学的各种其他领域产生兴趣。该项目将首先将三个已建成的基础设施和已知的相互依赖(包括物理和功能上的)归类为适合建模的“网络网络”表示。这项研究的一个关键部分还在于量化已建成的基础设施和社会系统之间的相互作用。因此，这些模型将通过城市“生态计量学”纳入社区层面的行为效应。“生态计量学”是一种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。这种对基础设施及其相互依存关系的现实核算，将得到对其可能面临的未来危险的现实估计的补充。研究的核心将使用基于网络的分析和计算方法来确定相互依赖的基础设施的(高维)动态状态的降维表示。研究这些恢复力指标如何在组件级别的网络压力下发生变化(例如，飓风后的洪水引起的)，将有助于确定现有相互依存的基础设施中的薄弱环节。还将探讨相反的情况--故意更改网络可能会提高恢复能力或加快已经出现故障的系统的恢复。

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.