This Faculty Early Career Development (CAREER) grant will investigate new methodologies to advance learning from post-windstorm reconnaissance data. Windstorms, such as hurricanes and tornadoes, continue to cost billions in economic losses each year in the United States, much of which is due to the performance of buildings. In response, researchers collect increasingly vast datasets documenting the post-windstorm state of buildings. These data have the potential to drive advancements in both fundamental science and engineering practice that will strengthen the resilience of buildings and communities and can reduce future losses and other impacts. The robust capabilities for capturing windstorm performance data vastly outweigh current capabilities for learning from this data, which are typically incomplete, biased, and ill-suited for efficient discovery and application of knowledge. This project will develop a robust, theory-guided, statistical inference framework for learning from post-windstorm data that will transform the scale to understand and predict windstorm damage, specifically for low-rise buildings. These advancements will spur the development and implementation of more effective windstorm risk mitigation and more robust education strategies, and further inform more efficient and intelligent post-disaster reconnaissance methodologies. An interactive outreach platform will be developed to translate the research findings to the general public and increase public awareness of the critical factors affecting windstorm performance. A new graduate and undergraduate student organization will be developed to foster inter-disciplinary collaboration within the disaster research community that will produce a new generation of engineers, social scientists, and policy makers that have a more holistic understanding of disasters and disaster risk mitigation. Data from this project will be archived and made publicly available in the Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (www.DesignSafe-ci.org). This grant supports the National Science Foundation (NSF) role in the National Windstorm Impact Reduction Program (NWIRP).
Windstorm performance of buildings is a function of a complex set of interacting factors that span meteorology, engineering, public policy, and socioeconomics that are not holistically understood. The specific goal of this research is to combine traditional data science with fundamental theory and expert knowledge to create a theory-guided, statistical inference framework that will enable efficient knowledge discovery from high-dimensional post-windstorm reconnaissance data. The project will utilize high quality post-windstorm datasets from recent windstorms collected by the NSF-supported Structural Extreme Events Reconnaissance network, enriched using additional data layers and human-machine techniques, to form robust testbeds for developing and piloting the new framework. The framework will build upon probabilistic graphical models, which allow established theory and expert knowledge to define known windstorm performance factors and their fundamental interrelationships, while focusing on causal inference as the goal rather than black box predictions. Ultimately, the research will enable a holistic understanding of the relative contributions of known windstorm performance factors, identify previously unknown or underestimated factors, and target new research areas supported by field observations.
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.
