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

该学院早期职业发展（CAREER）补助金将创建一个强大的定量和定性基础，使研究人员，学生和其他科学/工程专业人员从传统上独立的学科共同努力，制定更好的战略，以提高基础设施的弹性。提高基础设施弹性的一个重要步骤是评估众多自然灾害的风险，然后使用由此产生的风险见解来实时规划和响应事件。可能的危害评估是风险评估的起点。通常，这些评估侧重于地震或飓风等具体灾害。然而，重大灾害事件往往是多种危害的综合结果。不捕捉“多危害事件”的概率危害评估最终可能导致风险缓解和事件应对不理想。研究和教育方面的差距目前妨碍了全面综合的多种灾害评估。之所以存在这些差距，是因为用于各种自然灾害的方法是在相对孤立的情况下制定的。这导致了专业人员用于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，为空间分布的基础设施创建可动态更新的多灾害风险评估模型提供了坚实的基础。目标1将侧重于开发基于贝叶斯网络的公式，以评估多种空间分布的危害。这将提供一个统一的量化和图形结构，用于根据概率依赖性“链接”特定灾害模型。目标#2将重点关注实现贝叶斯网络所需的计算策略，用于在实际规模上对多个空间分布的危险（建模为随机场）进行概率评估。将使用统计和机器学习方法开发物理信息替代模型，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。代理模型开发研究将与基于项目的大学课程相结合。最后，目标#3将专注于创建一个动态更新的，多危害贝叶斯网络案例研究，以展示，验证，沟通，并寻求对项目成果的反馈。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.