权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
1635717
负责人：
Hiba Baroud
金额：
$ 24.89万
依托单位：
Vanderbilt University
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=1635717&HistoricalAwards=false
关键词：
Collaborative Research Bayesian Methods Data

项目摘要

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，在这些规划中，抵御和从加剧我们老化和脆弱的基础设施系统的中断中恢复的组合构成了弹性。根据国土安全部的说法，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社区在发生破坏性事件后的复原力，承认基础设施不是为其本身而存在，而是为社会(例如，公民、行业)服务，在某些情况下，复原性社区有助于保护建成的环境。一个有弹性的社区理想情况下应该能够利用有形基础设施有效地传达风险，协调恢复战略，以应对中断并从中断中恢复，并最终适应变化并从过去的中断中吸取教训。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)模拟基础设施网络性能的能力，以及(Ii)规划这些网络在中断后恢复的能力，重点是社区的复原力和经济生产力。研究方法由三部分组成。第一个组件开发了一种新的统计技术-分层贝叶斯核方法，它集成了随着数据的动态获取而提高预测精度的贝叶斯性质，增加了模型的特殊性并可以使非线性数据更易于管理的核函数，以及在稀疏和多样化的数据情况下从不同来源借用信息的分层性质，这在破坏性事件场景中是常见的。第二组件开发基础设施网络恢复优化公式，其利用基础设施恢复的数据驱动(并且动态更新)的分层贝叶斯核参数以及考虑模型参数的大小和动态性质的解决方案技术来最小化基础设施网络性能的较大影响。前两个综合组成部分适用于电力网络(衡量对社区的安全和复原能力的影响)和内河航道(衡量对多个行业的经济生产力的影响)，这是第三个组成部分，就基础设施网络复原力和恢复的影响提供了两个应用视角。