CAREER: Transcending Barriers between Natural Hazard Researchers, Educators, and Practitioners - An Integrative Approach to Multi-Hazard Probabilistic Assessment

这项学院早期职业发展(Career)补助金将创建一个强大的数量和质量基础，使来自传统独立学科的研究人员、学生和其他科学/工程专业人员能够共同努力，制定更好的战略，以提高基础设施的弹性。提高基础设施复原力的关键步骤包括评估来自众多自然灾害的风险，然后使用由此产生的风险洞察来实时计划和应对事件。概率危险评估是风险评估的起点。通常，这些评估侧重于特定的危险，如地震或飓风。然而，重大灾害事件往往是多种危险因素综合作用的结果。没有捕捉到“多危险事件”的概率风险评估最终可能导致次优的风险缓解和事件响应。目前，研究和教育方面的差距阻碍了对多种危险的全面综合评估。之所以存在这些差距，是因为应对各种自然灾害的方法是在相对孤立的情况下发展起来的。这导致了专业人员用来评估危险的工具以及如何在法规中教授、研究和解决危险评估的有意义的差异。该项目涉及围绕三个目标的研究和教育活动，这三个目标为为空间分布的基础设施创建动态可更新的、多种危险的风险评估模型提供了坚实的基础。目标1将专注于开发基于贝叶斯网络的公式，以评估多个空间分布的危害。这将提供一个统一的量化和图形结构，用于根据特定危险模型的概率依赖关系将其“链接”起来。目标2将侧重于实施贝叶斯网络所需的计算使能策略，以便在实际规模上对多个空间分布的危险(模拟为随机场)进行概率评估。将使用统计和机器学习方法开发物理知情的代理模型，以减少与生成贝叶斯网络所需的条件概率表相关的计算(处理)需求。代孕模型开发研究将与该主题的基于项目的大学课程相结合。最后，目标#3将专注于创建一个可动态更新的、多风险的贝叶斯网络案例研究，以演示、验证、交流项目结果并寻求反馈。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) grant will create a strong quantitative and qualitative foundation that enables researchers, students, and other science/engineering professionals from traditionally separate disciplines to work together to develop better strategies to improve the resilience of infrastructure. An essential step toward improving infrastructure resilience involves assessing risks from numerous natural hazards and then using the resulting risk-insights to plan for and respond to events in real-time. Probabilistic hazard assessments serve as the starting point for risk assessments. Typically, these assessments focus on specific hazards such as earthquakes or hurricanes. However, major disaster events often result from combinations of multiple hazards. Probabilistic hazard assessments that do not capture “multi-hazard events” can ultimately lead to suboptimal risk mitigation and event response. Research and education gaps currently prevent fully integrated multi-hazard assessments. These gaps exist because the approaches used for various natural hazards were developed in relative isolation. This has led to meaningful differences in the tools professionals use to assess hazards and how hazard assessment is taught, researched, and addressed in regulation. This project involves research and education activities centered around three aims that provide a strong foundation for creating dynamically updatable, multi-hazard risk assessment models for spatially-distributed infrastructure. Aim #1 will focus on developing Bayesian-network-based formulations to assess multiple, spatially-distributed hazards. This will provide a unified quantitative and graphical structure for “linking” hazard-specific models based on their probabilistic dependencies. Aim #2 will focus on computation enabling strategies needed to implement Bayesian networks for probabilistic assessments of multiple, spatially-distributed hazards (modeled as random fields) at practical scales. Physically-informed surrogate models will be developed using statistical and machine-learning methods to reduce the computational (processing) demands associated with generating conditional probability tables required by the Bayesian networks. Surrogate model development research will be coupled with a project-based university course on the subject. Finally, Aim #3 will focus on creating a dynamically updateable, multi-hazard Bayesian network case study to demonstrate, validate, communicate, and seek feedback on project outcomes.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.