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.

灾难是由于人类和基础设施的危害和脆弱性的综合作用而造成的。这位教师早期职业发展（职业）赠款旨在了解人们与基础设施之间的互动，以应对危害。这些相互作用通常会导致灾难期间和之后的意外结果。基础设施系统的失败会直接或间接影响其他基础设施，并导致社会经济部门的残酷影响。此外，缺乏对基础设施管理中社会和组织方面的考虑可能导致损害社区的意外后果。因此，这些相互作用是不确定的，并且随着时间的流逝而随着危害，开发和技术的响应而变化。如果我们可以捕获这种相互依存的动态，则可以主动干预以防止失败，减少损失并改善恢复。该项目的目标是（i）开发下一代动态网络，以建模政策，基础设施和社区之间不确定的相互依存关系，（ii）通过社区参与和以人为中心的研究项目和（iii）提高通过媒体，艺术和女子艺术舞蹈活动来提高公众对灾难的认识。该项目符合NSF促进科学进步并促进民族福利的使命。具体而言，这项综合研究和教育的结果将改变政策制定，以确保在短期和长期风险下社区的韧性和可持续性。研究方法建立在统计网络模型中，该模型基于对结构或历史数据的部分观察，对未来网络结构进行了概率估计。这些模型提供了一种编码管理网络结构的潜在参数的方法，从而为推理和预测提供了优势。该项目将开发一种新的统计网络模型，以适应多个网络的动态。这些模型将由利益相关者告知，并与经验方法和建模技术相结合，例如决策分析和贝叶斯方法，以捕获灾难中人类行为。将顶点和边缘协变量的掺入将使用用于预测分析的统计学习方法来完成。这些新模型将使用两个城市的数据驱动方案来实施，并具有增加城市化和自然危害，以提高有关在不同情况下可持续性和弹性评估多样性的知识。将开发用于网络预测分析的算法，以量化可持续的弹性指标。通过跨学科研究项目和国际经验的身临其境的教育机会将吸引学生的社区，并增强他们的学习经验，同时为灾难的研究和以人为本的解决方案做出贡献。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来审查审查标准来通过评估来诚实地通过评估来诚实地支持。

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