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Project Type | Field research | Dataset | Paper | Presentation | Poster |
Natural Hazard Type(s) | Hurricane/Tropical Storm, Storm Surge, Flood |
Facilities | |
Event(s) | Hurricane Ian | Fort Myers Beach, FL | 2022-09-28 ― 2022-09-28 | Lat 26.451555 long -81.948273 |
Awards | Center for Risk-Based Community Resilience Planning | 70NANB20H008 | National Institute of Standards and Technology Center for Risk-Based Community Resilience Planning | 70NANB15H044 | National Institute of Standards and Technology Hurricane Coastal Impacts Program | N00014-21-1-2184 | National Oceanographic Partnership Program Coastal Resilience Center | 2015-ST061-ND0001-01 | US Department of Homeland Security |
Keywords | Hurricane Ian, Damage assessment , Structural damage, First-floor elevation, Coastal buildings |
Several field work and reconnaissance data -primarily pre- and post-storm street-level and aerial imagery- were used to create a comprehensive collection of datasets for virtual damage assessment (VDA) and first-floor elevation (FFE) measurements of 3,408 building structures impacted by Hurricane Ian (2022) in Fort Myers Beach, Florida. The methodology was implemented using engineering students and was validated through a cross-comparison between student and expert opinions. The VDA data includes component-based damage assessment on a damage state (DS) scale from no damage (DS0) to complete damage (DS6) for roof, walls, elevated floors, windows and doors, attachments, and foundations to estimate the overall damage to the structure. The FFE data includes foundation type based National Flood Insurance Program (NFIP) guidelines which are managed by the Federal Emergency Management Agency (FEMA). This dataset also integrates publicly available data utilized in this project, including: (1) National Structural Inventory (NSI), (2) Lee County Damage Assessment (LCDA), (3) Tax Assessor (TA), (4) Florida Elevation Certificate (FEC), (5) surge/wave data based on high-water marks (HWMs). This collection of datasets can be utilized to improve understanding of hurricane damage to buildings, which can lead to the development of reliable models to predict damage in coastal communities.