Research & Initiatives
Community resilience is the ability of a community to absorb, "bounce back from", and adapt to a disruption. In our research, this disruption is typically a meteorological natural hazard.
Our modeling efforts utilize data-based (machine learning) and physics-based methods. The Hurricane Impact Level (HIL) Model was built using an ensemble of artificial neural networks. Whereas, structural damage and loss modeling from wind hazards for NIST's IN-CORE consists of physics-based fragility curves.
Using the HIL Model to forecast the impact of Hurricane Harvey real-time
Loss curves for a two-story government building found in IN-CORE
Our research group utilizes data analytics to further our understanding of socio-physical interactions pertaining to natural hazards research. Dr. Pilkington's initial work in this area involves using artificial neural networks in combination with graph theory to explore such non-linear and complex relationships.
Structural Extreme Events Reconnaissance
Dr. Pilkington also serves as a volunteer member with NSF's StEER and conducts post-event damage assessments, both virtually and in the field, while also sitting on the Wind Advisory Board.