CRISP Type 2/Collaborative Research: Scalable Decision Model to Achieve Local and Regional Resilience of Interdependent Critical Infrastructure Systems and Communities

CRISP 类型 2/协作研究：可扩展的决策模型，以实现相互依赖的关键基础设施系统和社区的本地和区域弹性

• 批准号: 1638346
• 负责人: Paolo Gardoni
• 金额: $ 79.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1638346&HistoricalAwards=false
• 关键词: CRISP; Type; Collaborative; Research; Scalable

The US economy and social wellbeing depend on interdependent critical infrastructure systems (ICISs) such as transportation, energy, water, and food systems. These ICISs shape the countrys ability to meet community needs often successful, but not for all, and are susceptible to disruptions due to extreme natural events. This interplay between normal operation, chronic issues, and disaster-induced challenges is clearly evident when considering food security issues. Food access and affordability are persistent problems for more than 14 percent of Americans in normal times and are greatly exacerbated following disasters. Frameworks for understanding ICIS interdependencies, their interface with social and economic networks in response to natural hazards, and their roles in disaster recovery for vulnerable populations and food security are nascent. The food security of a community is a function of the pre-event vulnerabilities and the resilience of its food distribution network including the vulnerabilities of its infrastructural systems in isolation and their interdependencies. Furthermore, the demands posed by different hazards, the capacity of each physical network and system to respond to these demands, and the interactions between physical and social systems are highly uncertain. Accordingly, risk-informed approaches that can guide decision methods are crucial to characterize demand and impact on a community, to predict community response, and for designing community infrastructure systems that are resilient. Well-integrated decision methods that account for and integrate the performance of different ICISs in response to disasters have broad impacts. First, such methodologies will better frame questions on disaster mitigation and recovery, and will facilitate disaster planning activities and training for various disaster scenarios. Second, they will encourage policies that address chronic and acute food-security issues, balancing the mitigation of vulnerability with the promotion of resiliency. Finally, they will foster a shared language among social, behavioral, and economic (SBE) scientists, computational scientists, and engineers on the causes and characterization of hazards and risks and mitigation solutions. This project will engage a diverse set of students, including women and minorities, and in student-centered learning. It will integrate research and education throughout the project, and effectively disseminate the results. The methodologies developed will be integrated into courses such as Engineering Risk Analysis and Structural Reliability, Disaster Mitigation and Recovery and Planning Methods, and Risk and Regulation and into two NSF Research Experience for Undergraduate (REU) summer institutes which blend geography, computer science, health, planning and social science undergraduate students in food security, disparities, and health research projects.This research will develop a decision platform that integrates computational models of ICISs at different spatial and temporal scales. These computational models will focus on the food distribution networks and include analytics of the socioeconomic causes of vulnerability. The decision platform may be used to examine issues related to reducing the risks associated with extreme hazards while enhancing community resilience with respect to food security. The project brings together three distinct disciplines: Engineering, SBE sciences, and Computer/Computational Sciences. Achieving project goals requires a deep collaboration between these three broad disciplines. Engineering is needed to understand and model the physical components of each sector and their interdependencies. SBE sciences are essential to understand and model food distribution from wholesale to households with a focus on vulnerable populations. Computer and Computational Science are needed to develop comprehensive models representing communities and their infrastructure and are the basis for assessing policy and organizational interventions that lead to greater robustness and resilience. The interdisciplinary nature of this research will also forge new channels of communication through models that integrate social and physical aspects of risk and vulnerability.

美国经济和社会福祉依赖于相互依存的关键基础设施系统（ICIS），如交通、能源、水和食品系统。这些ICIS塑造了该国满足社区需求的能力，通常是成功的，但不是所有的，并且容易受到极端自然事件的干扰。在考虑粮食安全问题时，正常运作、长期问题和灾害引起的挑战之间的相互作用显而易见。在正常时期，食物获取和负担能力是超过14%的美国人的持续问题，在灾害发生后会大大加剧。理解ICIS相互依存关系的框架，它们与社会和经济网络在应对自然灾害方面的接口，以及它们在弱势群体灾后恢复和粮食安全方面的作用正在形成。一个社区的粮食安全取决于其粮食分配网络的事前脆弱性和复原力，包括其基础设施系统孤立存在的脆弱性及其相互依存性。此外，不同灾害所带来的需求，每个物理网络和系统响应这些需求的能力，以及物理系统和社会系统之间的相互作用都是高度不确定的。因此，能够指导决策方法的风险知情方法对于确定需求和对社区的影响、预测社区反应以及设计具有复原力的社区基础设施系统至关重要。考虑到并整合不同ICIS在应对灾害方面的表现的综合决策方法具有广泛的影响。第一，这种方法将更好地确定关于减灾和灾后恢复的问题，并将促进灾害规划活动和针对各种灾害情况的培训。第二，它们将鼓励制定政策，解决长期和严重的粮食安全问题，在减轻脆弱性与促进复原力之间取得平衡。最后，他们将在社会，行为和经济（SBE）科学家，计算科学家和工程师之间培养一种关于危害和风险的原因和特征以及缓解解决方案的共享语言。该项目将吸引各种各样的学生，包括妇女和少数民族，并以学生为中心的学习。它将在整个项目中整合研究和教育，并有效地传播成果。开发的方法将被整合到课程，如工程风险分析和结构可靠性，减灾和恢复和规划方法，风险和监管，并进入两个NSF研究经验的本科生（REU）暑期学院，融合地理，计算机科学，健康，规划和社会科学本科生在粮食安全，差距，这项研究将开发一个决策平台，整合不同空间和时间尺度的ICIS计算模型。这些计算模型将侧重于粮食分配网络，并包括分析脆弱性的社会经济原因。该决策平台可用于审查与减少极端灾害相关风险有关的问题，同时增强社区在粮食安全方面的复原力。该项目汇集了三个不同的学科：工程，SBE科学和计算机/计算科学。实现项目目标需要这三个广泛学科之间的深入合作。工程需要理解和建模每个部门的物理组件及其相互依赖性。SBE科学对于理解和模拟从批发到家庭的粮食分配至关重要，重点是弱势群体。需要计算机和计算科学来开发代表社区及其基础设施的综合模型，并且是评估政策和组织干预措施的基础，从而提高稳健性和弹性。这项研究的跨学科性质还将通过整合风险和脆弱性的社会和物理方面的模型建立新的沟通渠道。

项目成果

A ground-up approach to estimate the likelihood of business interruption

估计业务中断可能性的彻底方法

• DOI: 10.1016/j.ijdrr.2019.101314
• 发表时间: 2019
• 期刊: International Journal of Disaster Risk Reduction
• 影响因子: 5
• 作者: Nocera, Fabrizio;Gardoni, Paolo
• 通讯作者: Gardoni, Paolo

Classification and mathematical modeling of infrastructure interdependencies

基础设施相互依赖性的分类和数学建模

• DOI: 10.1080/23789689.2020.1753401
• 发表时间: 2020
• 期刊: Sustainable and Resilient Infrastructure
• 影响因子: 5.9
• 作者: Sharma, Neetesh;Nocera, Fabrizio;Gardoni, Paolo
• 通讯作者: Gardoni, Paolo

Resilience analysis: a mathematical formulation to model resilience of engineering systems

• DOI: 10.1080/23789689.2017.1345257
• 发表时间: 2018-04
• 期刊: Sustainable and Resilient Infrastructure
• 影响因子: 5.9
• 作者: Neetesh Sharma;Armin Tabandeh;P. Gardoni

Time-Dependent Probability of Exceeding a Target Level of Recovery

超过目标恢复水平的时间相关概率

• DOI: 10.1061/ajrua6.0001019
• 发表时间: 2019
• 期刊: Part A: Civil Engineering
• 作者: Nocera, Fabrizio;Gardoni, Paolo;Cimellaro, Gian Paolo
• 通讯作者: Cimellaro, Gian Paolo