Image by Sheri Hooley

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.


Drone Reconnaissance

Our Ghost Robotics V60 legged ground drone is intended for surveys of damaged or destroyed infrastructure that may not be safe for human entry. If you are interested in utilizing our ground drone for a project, please contact Dr. Pilkington.


Survey, analysis, and modeling efforts by our group has resulted in uniquely curated datasets with infrastructure, demographic, and meteorological variables, among others. Please contact Dr. Pilkington if you are interested in data sets related to our published work. 

Image by Chris Gallagher