Civil Engineering Professor Awarded NSF Early CAREER Grant for Novel Wind Research

photo of civili engineering professor pedro fernandez-caban in front of palm trees at the famu-fsu college of engineering

Pedro Fernández-Cábán, a FAMU-FSU College of Engineering professor, has been recognized by the National Science Foundation for his early-career research. (Scott Holstein/FAMU-FSU Engineering)

Pedro Fernández-Cábán, an assistant professor in the FAMU-FSU College of Engineering’s Department of Civil and Environmental Engineering, recently received NSF’s prestigious CAREER Award, a grant given to promising up-and-coming researchers and future faculty leaders. The five-year, $548,702.00 grant funds his work focusing on the nature of extreme winds and the impact on the environment.

Faculty Early Career Development (CAREER) awards help support junior faculty for their potential to serve as role models in research, teaching and leadership in their field. They are among the most prestigious awards granted to junior faculty by the science organization. 

“I am very honored and humbled to have received this award,” Fernández-Cábán said. “The grant will provide stable support to integrate my educational and research goals aimed at accelerating the adoption of advanced machine learning tools to enhance wind hazard resilience.”

Fernández-Cábán is investigating the site-dependent nature of extreme wind fields that occur during landfalling hurricanes and their impact on the built environment. His team will use advanced meta-learning algorithms to predict the geospatial location and intensity of extreme wind gusts that cause structural damage to civil infrastructure. 

“During landfalling hurricanes, wind hazard conditions vary significantly in space and time,” Fernández-Cábán said. “The limited availability of ground-level wind data collected during these storms makes it challenging for researchers to identify coastal sites experiencing the strongest wind gusts.” 

Fernández-Cábán’s research will leverage rich datasets of ground-level wind observations collected during landfalling hurricanes and experimental data generated in wind tunnels to train the meta-learning models. The team will apply novel wind tunnel testing approaches that can simulate real-world wind hazard conditions and their interaction with complex terrain features of coastal communities. The experimental work will utilize the Natural Hazards Engineering Research Infrastructure (NHERI) Boundary Layer Wind Tunnel at the University of Florida.

“This work will apply advanced machine learning models to reliably infer wind hazard conditions where field data is unavailable,” Fernández-Cábán said.

The research contributes to the National Windstorm Impact Reduction Program (NWIRP) established by Congress to improve the understanding of windstorms and their impacts and to develop and encourage the implementation of cost-effective mitigation measures to reduce those impacts. 

The wind tunnel and meta-learning data generated from Fernández-Cábán’s project will be archived and made publicly available in the Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot.


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