CAREER: Fusing Meta-Learning Systems and Field Observations to Enhance the Simulation of Extreme Winds and their Impact on Civil Infrastructure

This Faculty Early Career Development (CAREER) award will fund research that intends to advance fundamental understanding of the site-dependent nature of extreme wind fields occurring during hurricanes and their impact on the built environment. The research is motivated by the palpable differences between ground-level wind observations collected during field research activities and the idealized wind flow conditions simulated in traditional boundary layer wind tunnels.

This research will leverage meta-learning systems, full-scale anemometric data collected during landfalling hurricanes, and high-throughput experimental testing to simulate real-world wind hazard conditions and their interaction with complex terrain features of coastal communities. The award will expand the geospatial coverage of inherently sparse ground-based anemometric measurements to support post-storm field reconnaissance teams in synthesizing observed structural damage to site-specific wind hazard conditions.

The research will be integrated with educational and public engagement activities, including the development of learning modules to accelerate the implementation of artificial intelligence-based tools into civil engineering curricula, hosting Learning-to-Learn (L2L) workshops for middle and high school students from the City of Tallahassee, and fostering public and stakeholder engagement from coastal communities in the Florida Panhandle. Data generated from this project will be archived and made publicly available in the Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https:/www.DesignSafe-c.org).

This award will contribute to the National Science Foundation's role in the National Windstorm Impact Reduction Program (NWIRP).

The research activities are intended to drive broader societal outcomes that directly address enduring challenges related to the nation's ability to mitigate and adapt to the ever-changing threat of extreme wind hazard events. The specific goals of this research include (i) the recreation of real-world hurricane wind records at multiple geometric scales using a multi-fan flow-control instrument, (ii) the physical simulation of real-world terrain morphology of coastal communities using high-resolution digital surface models (DSM), (iii) the integration of meta-learning algorithms to extract salient features of distal and proximate terrain characteristics that influence ground-level hurricane winds, and (iv) the development of a site-specific wind (component- and building-level) load inference tool based on realistic wind flow traces.

The experimental work will utilize the NHERI Boundary Layer Wind Tunnel at the University of Florida.

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.