Collaborative Research: Bayesian Methods for the Data-Driven Recovery of Networks: Measuring Impact and Building Resilience in Infrastructures and Communities

合作研究：用于数据驱动的网络恢复的贝叶斯方法：衡量基础设施和社区的影响并建立弹性

• 批准号: 1635813
• 负责人: Kash Barker
• 金额: $ 21.42万
• 依托单位: University of Oklahoma Norman Campus
• 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=1635813&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; Methods; Data

The US government has increasingly emphasized resilience planning for critical infrastructure, where the combination of withstanding and recovering from disruptions that exacerbate our aging and vulnerable infrastructure systems, constitutes resilience. According to the Department of Homeland Security, the resilient operation of critical infrastructures is "essential to the Nation's security, public health and safety, economic vitality, and way of life." Of particular interest recently is an emphasis on the resilience of communities after a disruptive event, acknowledging that infrastructures do not exist for their own sake but serve society (e.g., citizens, industries), and in some cases, resilient communities assist in protecting the built environment. A resilient community would ideally be able to use the physical infrastructure to effectively communicate risk and coordinate recovery strategies to respond to and recover from disruptions, and ultimately adapt to change and learn from past disruptions. The objective of this work is to develop a new data-driven optimization framework to improve (i) the ability to model the performance of infrastructure networks, and (ii) the ability to plan for the recovery of these networks after a disruption, with an emphasis on community resilience and economic productivity. The research approach is composed of three components. The first component develops a new statistical technique, the hierarchical Bayesian kernel method, which integrates the Bayesian property of improving predictive accuracy as data are dynamically obtained, the kernel function that adds specificity to the model and can make nonlinear data more manageable, and the hierarchical property of borrowing information from different sources in sparse and diverse data situations which are common in disruptive events scenarios. The second component develops an infrastructure network recovery optimization formulation that minimizes the larger impact of infrastructure network performance with data-driven (and dynamically updated) hierarchical Bayesian kernel parameters of infrastructure recovery, along with solution techniques that account for the size and dynamic nature of model parameters. The application of the first two integrated components to electric power networks (where impact is measured on the safety and resilience of the community) and inland waterways (where impact is measured on economic productivity across multiple industries), constitutes the third component, offering two application perspectives on the impact of infrastructure network resilience and recovery.

美国政府越来越强调关键基础设施的弹性规划，其中抵御和恢复加剧我们老化和脆弱的基础设施系统的破坏，构成了弹性。据国土安全部称，关键基础设施的弹性运行“对国家安全、公共卫生和安全、经济活力和生活方式至关重要”。最近特别令人感兴趣的是强调破坏性事件后社区的恢复力，承认基础设施不是为了自身而存在，而是为社会（例如公民、工业）服务，并且在某些情况下，恢复力社区有助于保护建筑环境。理想情况下，一个有弹性的社区将能够使用物理基础设施来有效地沟通风险并协调恢复策略，以响应中断并从中断中恢复，并最终适应变化并从过去的中断中学习。这项工作的目标是开发一个新的数据驱动的优化框架，以提高（i）基础设施网络性能建模的能力，以及（ii）在中断后规划这些网络恢复的能力，重点是社区复原力和经济生产力。研究方法由三个部分组成。第一个组件开发了一种新的统计技术，即分层贝叶斯核方法，该方法集成了动态获取数据时提高预测准确性的贝叶斯特性、增加模型特异性并使非线性数据更易于管理的核函数，以及在破坏性事件场景中常见的稀疏和多样化数据情况下从不同来源借用信息的分层特性。第二个组件开发基础设施网络恢复优化公式，通过数据驱动（并动态更新）的基础设施恢复分层贝叶斯内核参数以及考虑模型参数大小和动态性质的解决方案技术，最大限度地减少基础设施网络性能的较大影响。前两个集成组件在电力网络（衡量对社区安全和复原力的影响）和内陆水道（衡量对多个行业的经济生产力的影响）的应用构成了第三个组件，提供了关于基础设施网络复原力和恢复影响的两个应用视角。

项目成果

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

• DOI: 10.1111/risa.13372
• 发表时间: 2019-08
• 期刊: Risk Analysis
• 影响因子: 3.8
• 作者: Andrea Garcia Tapia;Mildred Suarez;J. Ramírez-Márquez;K. Barker

Community vulnerability perspective on robust protection planning in interdependent infrastructure networks

• DOI: 10.1177/1748006x21991038
• 发表时间: 2021-02
• 期刊: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
• 作者: Hannah Lobban;Y. Almoghathawi;Nazanin Morshedlou;K. Barker

A heuristic approach to an interdependent restoration planning and crew routing problem

• DOI: 10.1016/j.cie.2021.107626
• 发表时间: 2021-09-08
• 期刊: COMPUTERS & INDUSTRIAL ENGINEERING
• 影响因子: 7.9
• 作者: Tajik, Nazanin;Barker, Kash;Ermagun, Alireza
• 通讯作者: Ermagun, Alireza

Investing in Absorptive Capacity in Interdependent Infrastructure and Industry Sectors

投资于相互依赖的基础设施和工业部门的吸收能力

• DOI: 10.1061/(asce)is.1943-555x.0000514
• 发表时间: 2020
• 期刊: Journal of Infrastructure Systems
• 影响因子: 3.3
• 作者: Darayi, Mohamad;Pant, Raghav;Barker, Kash;Morshedlou, Nazanin
• 通讯作者: Morshedlou, Nazanin

Measuring Community and Multi-Industry Impacts of Cascading Failures in Power Systems

• DOI: 10.1109/jsyst.2017.2768603
• 发表时间: 2018-12
• 期刊: IEEE Systems Journal
• 影响因子: 4.4
• 作者: Bing Li;K. Barker;G. Sansavini