Date of Award


Document Type


Degree Name

Master of Science (MS)


Civil Engineering

Committee Chair/Advisor

Dr. Weichiang Pang

Committee Member

Dr. Michael Stoner

Committee Member

Dr. Yongjia Song

Committee Member

Prof. Dustin Albright


This research introduces an advanced framework which employs parametric wind field models for peak wind speeds, and building fragility curves, loss functions, and demographic data to estimate for estimating housing damage and loss. The uninhabitable units immediate displaced households, short-term and long-term shelter need households are determined. with a particular focus on those eligible for FEMA assistance. The framework's validity is reinforced by a high correlation in the analysis of recent hurricane events between estimated numbers of displaced households and actual FEMA aid recipients, where FEMA aids about 20-60% of the predicted long-term displaced households. A novel application of the model simulates nine 1989 Hugo-like storms passing through Charleston, analyzing Hurricane Hugo as a "below average" event in economic losses over a 125-year period using Mean Return Interval analysis. The model also incorporates multiple time step realizations and an ensemble of 903 potential hurricane tracks for each pre-landfall day, highlighting the variability in storm trajectory and intensity. This research is critical for disaster management practitioners, urban planners, and policymakers, providing actionable insights to improve disaster response strategies and enhance community resilience. The study is structured into six chapters, starting with an introduction, a literature review identifying research gaps, detailed methodology, case studies comparing model predictions with FEMA's responses, analysis of simulated storms, and concluding with recommendations for future research. This comprehensive approach allows stakeholders to understand and manage hurricane impacts more effectively, emphasizing the importance of continuous monitoring and real-time updates in hurricane forecasting.

Author ORCID Identifier




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