RAPID/Collaborative Research: Performance of Low-Rise Large-Volume Buildings in Florida during 2018 Hurricane Michael

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Low-rise, large-volume (LRLV) metal buildings are a critical component to community and national resilience, often functioning as school auditoriums or gymnasiums, military or civilian aircraft hangars, distribution centers, supermarkets, churches, industrial and manufacturing buildings, and storage facilities protecting high-value property. Unfortunately, many of these structures suffered catastrophic collapses or irreparable damage following Hurricane Michael, which made landfall south of Panama City, Florida on October 10, 2018, as a Category 4 hurricane. Preliminary assessments by the NSF-supported Structural Extreme Events Reconnaissance network found that damage to these structures was common and often disproportionate to surrounding buildings, indicating a knowledge gap in the understanding of the dynamic loads on these structures and/or the mechanisms of structural response to these loads during extreme wind events. This Grant for Rapid Response Research (RAPID) will support field deployments to quickly and precisely capture the post-hurricane damage state of LRLV buildings in the form of high-resolution 3D point clouds, by means of terrestrial and airborne LIDAR and photogrammetry, and forensic structural engineering analysis. Post-processing and analysis of the data will inform advanced computational models of wind load and structural response for these buildings, which will ultimately enable safer and more efficient designs. By enabling a better understanding of the complex dynamic behavior of these buildings under wind loads, the findings from this project can inform future designs to reduce the frequency of LRLV building failures in future extreme wind events. The field reconnaissance and analysis will train undergraduate and graduate engineers in forensic engineering methods and provide high quality case studies that can be used by the engineering education community. Data collected from this award will be archived in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-CI.org). During hurricane-force winds, the dimensions of LRLV metal buildings can exceed the integral length scales of the turbulent flow, producing incoherent gust structures over the surface of the building. The effects of this phenomenon on peak structural forces, and particularly the internal pressure response, are not well understood. The goals of this project are to: 1) preserve the precise post-hurricane condition of LRLV buildings impacted by Hurricane Michael prior to cleanup, 2) assess the extreme wind conditions to which the LRLV buildings were subjected, 3) conduct preliminary assessments of the primary drivers of LRLV building failures, and 4) position future research efforts for conducting advanced wind load and structural analysis simulations to address the key knowledge gaps that have led to large failure rates. Data gathering excursions (3-5 days) in Florida will use multiple sensing technologies to quickly gather detailed structural information on target structures including terrestrial LIDAR and measurements, and drone-based LIDAR and imagery. After reducing the data into 3D models, the analysis process will focus on parameters including building volume, unprotected opening size, building orientation with respect to primary wind direction, resonance of internal pressure, structural system (gravity and lateral), structure age, and pre-event condition. This study will address a key knowledge gap in hurricane-induced wind loads on LRLV buildings while simultaneously investigating the structural system response to these loads. The holistic approach will advance scientific understanding of the phenomena driving the failures of these systems and inform more resilient building designs for future events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.