CAREER: Policy-Infrastructure-Community Interdependencies: The Next Frontiers in Dynamic Networks

职业：政策-基础设施-社区相互依赖关系：动态网络的下一个前沿

基本信息

批准号：
1944559
负责人：
Hiba Baroud
金额：
$ 50万
依托单位：
Vanderbilt University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944559&HistoricalAwards=false
关键词：
CAREER Policy Infrastructure Community Interdependencies

项目摘要

Disasters result from the combined effect of hazards and vulnerability of humans and infrastructures. This Faculty Early Career Development (CAREER) grant seeks to understand the interactions between people and infrastructures in response to hazards. These interactions often lead to unexpected outcomes during and in the aftermath of disasters. The failure of an infrastructure system can directly or indirectly affect other infrastructures and result in crippling effects in socio-economic sectors. In addition, a lack of consideration of social and organizational aspects in infrastructure management can lead to unintended consequences that harm communities. As such, these interactions are uncertain, and they change over time in response to hazards, development, and technology. If we can capture the dynamics of such inter-dependencies, we can proactively intervene to prevent cascading failures, reduce losses, and improve recovery. The objectives of this project are to (i) develop the next generation of dynamic networks to model the uncertain inter-dependencies between policy, infrastructure, and the community, (ii) train future engineering practitioners and researchers through community engagement and human-centered research projects, and (iii) increase public awareness of disaster preparedness through media, arts, and Girl Scouts activities. This project aligns with NSF’s mission to promote the progress of science and to advance the national welfare. Specifically, the outcome of this integrated research and education will transform policy making to ensure the resilience and sustainability of communities under short- and long-term risks.The research approach is founded in statistical network models which provide a probabilistic estimation of future network structures based on partial observation of the structure or historical data. These models provide an approach to encode latent parameters that govern network structures, offering advantages for inference and prediction. This project will develop a new class of statistical network models that accommodates the dynamics of multiple networks. The models will be informed by stakeholders and integrated with empirical approaches and modeling techniques such as decision analysis and Bayesian methods to capture human behavior during disasters. Incorporation of vertex and edge covariates will be accomplished using statistical learning methods for predictive analytics. These new models will be implemented using data-driven scenarios of two cities with increased urbanization and natural hazards to advance knowledge on the diversity of sustainability and resilience assessments in different contexts. Algorithms for network predictive analytics will be developed to quantify sustainable resilience indicators. Immersive educational opportunities through interdisciplinary research projects and international experiences will engage students with communities and enhance their learning experience while contributing to research and human-centered solutions to disasters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

灾难是由人类和基础设施的危害和脆弱性的综合影响造成的。该学院早期职业发展（职业）资助旨在了解人类和基础设施之间在应对危害时的相互作用，这些相互作用往往会导致意外的结果。基础设施系统的故障可能会直接或间接影响其他基础设施，并对社会经济部门造成严重影响。此外，基础设施管理中缺乏对社会和组织方面的考虑可能会导致损害社区的意外后果。。因此，这些相互作用是不确定的，并且会随着时间的推移而变化，以应对危害、发展和技术。如果我们能够捕捉这种相互依赖性的动态，我们就可以主动干预，以防止级联故障、减少损失并提高恢复能力。该项目的目标是（i）开发下一代动态网络来模拟政策、基础设施和社区之间不确定的相互依赖关系，（ii）通过社区参与和以人为本的研究来培训未来的工程从业者和研究人员项目，以及 (iii) 增加公众通过媒体、艺术和女童子军活动提高人们对备灾的认识该项目符合国家科学基金会促进科学进步和促进国家福利的使命。具体来说，这一综合研究和教育的成果将改变政策制定，以确保该研究方法建立在统计网络模型的基础上，该模型基于对结构或历史数据的部分观察来提供对未来网络结构的概率估计。这些模型提供了一种编码方法。控制网络结构的潜在参数，提供该项目将开发一类新型统计网络模型，该模型将由利益相关者提供信息，并与决策分析和贝叶斯方法等经验方法和建模技术相结合，以捕获人类信息。将使用预测分析的统计学习方法来实现顶点和边缘协变量的结合，这些新模型将使用城市化和自然灾害加剧的两个城市的数据驱动来实现，以增进对可持续性场景多样性的了解。评估不同环境下的复原力。将开发网络预测分析算法来量化可持续复原力指标，通过跨学科研究项目和国际经验提供沉浸式教育机会，让学生参与社区活动并增强他们的学习体验，同时为研究和以人为本的灾害解决方案做出贡献。该奖项是 NSF 的法定奖项。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modeling Uncertain and Dynamic Interdependencies of Infrastructure Systems Using Stochastic Block Models

DOI：
10.1115/1.4046472
发表时间：
2020-06
期刊：
影响因子：
0
作者：
Jin-Zhu Yu;H. Baroud
通讯作者：
Jin-Zhu Yu;H. Baroud

Quantifying the Interdependency Strength Across Critical Infrastructure Systems Using A Dynamic Network Flow Redistribution Model.

使用动态网络流量重新分配模型量化关键基础设施系统的相互依赖性强度。

DOI：
发表时间：
2020
期刊：
Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference.
影响因子：
0
作者：
Wang, Y.;Yu, J.;Baroud, H.
通讯作者：
Baroud, H.

Sources of uncertainty in interdependent infrastructure and their implications

相互依赖的基础设施的不确定性来源及其影响

DOI：
10.1016/j.ress.2021.107756
发表时间：
2021
期刊：
Reliability Engineering & System Safety
影响因子：
8.1
作者：
Reilly, Allison C.;Baroud, Hiba;Flage, Roger;Gerst, Michael D.
通讯作者：
Gerst, Michael D.

Comparing topology-based and flow-based resilience assessment of interdependent infrastructure networks

比较相互依赖的基础设施网络的基于拓扑和基于流的弹性评估

DOI：
发表时间：
2022
期刊：
13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022
影响因子：
0
作者：
Yu, Jinzhu;Wang, Yu;Baroud, Hiba
通讯作者：
Baroud, Hiba