Collaborative Research: A Data-centric Uncertainty-informed Framework for Resilience Analytics of Critical Infrastructure Under Extreme Climate Events

协作研究：以数据为中心、基于不确定性的框架，用于极端气候事件下关键基础设施的复原力分析

基本信息

批准号：
1826155
负责人：
Mazdak Tootkaboni
金额：
$ 22.43万
依托单位：
University of Massachusetts, Dartmouth
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826155&HistoricalAwards=false
关键词：
Collaborative Research Data centric Uncertainty

项目摘要

The United States' critical infrastructure and the communities that rely on their services are increasingly prone to climatic risks, with widespread impacts that are often followed by lengthy and costly restoration efforts. There is a fundamental need for scalable and accurate prediction models of natural hazard risks at local and regional scales to better assess and manage the resilience of our nation's infrastructure. The outcome of this research is expected to help policy makers and infrastructure operators characterize infrastructure resilience under various uncertain future scenarios and identify the optimal adaptation or mitigation strategies that result in maximum resilience gain in the system. In addition, this project possesses great potential for other positive societal impacts by educating the next generation of scholars in hazard modeling through a truly interdisciplinary, research-integrated educational program, a commitment to increased diversity in workforce training and broad dissemination of the results to scientific communities and stakeholders.This research project aims to advance the theory and practice of resilience engineering through establishing a pluralistic, data-centric and uncertainty-informed framework to efficiently characterize the multi-dimensional infrastructure resilience under stochastic hazards as well as plausible infrastructure evolution (due to adaptation or mitigation strategies) and climate change scenarios. This will be done through implementing the three key objectives of: (1) creating an accurate and multi-paradigm hurricane risk model, (2) establishing an accurate predictive framework for resilience analytics of critical infrastructure, based on a multi-dimensional Bayesian algorithm, and (3) leveraging recent advancements in stochastic analysis - based on Polynomial Chaos surrogates - to both fully characterize the uncertainties associated with the multi-dimensional resilience model, and implement computationally efficient scenario-based sensitivity analysis. Successful implementation of this project will yield a significant breakthrough in resilience modeling by enabling a scalable, accurate, and multi-dimensional assessment of infrastructure and community resilience; with rigorously and efficiently accounting for uncertainties.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.

美国的关键基础设施和依赖其服务的社区越来越容易出现气候风险，并具有广泛的影响，这些影响通常会持续漫长而昂贵的恢复工作。在本地和区域范围内，基本需要对自然危害风险的可扩展和准确的预测模型，以更好地评估和管理国家基础设施的弹性。这项研究的结果有望帮助政策制定者和基础设施运营商在各种不确定的未来场景下表征基础架构的弹性，并确定最佳的适应性或缓解策略，从而最大程度地提高系统中的弹性。此外，该项目通过通过真正的跨学科，研究综合的教育计划来教育危险建模中的下一代学者，具有巨大的潜力，这是对劳动力培训的多样性的承诺以及对科学社区和利益相关者的广泛传播的承诺。有效地表征在随机危害下以及合理的基础设施演变（由于适应或缓解策略）和气候变化情景下的多维基础设施的弹性以及合理的基础设施演变和气候变化情景。这将通过实施以下三个关键目标来完成：（1）创建准确且多范式的飓风风险模型，（2）基于多维贝叶斯算法的多维贝叶斯算法，以及（3）在整体表现方面，建立一个准确的弹性基础结构的弹性分析框架 - 基于多维的贝叶斯算法 - （3）与多维弹性模型相关的不确定性，并实施基于计算有效的方案的灵敏度分析。该项目的成功实施将通过实现对基础设施和社区弹性的可扩展，准确和多维评估，从而在弹性建模中取得重大突破；该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评论标准来评估的，这奖项反映了NSF的法定任务，并被认为值得支持。