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Abstract. Research often neglects historic and ageing infrastructure when investigating the impact of extreme wind loading and structural strengthening. This is exemplified by the ASCE 7-22 standard in the US that prescribes design loads for tornado hazard, which currently does not apply to Risk Categories (RC) 1 and 2, comprising a significant proportion of historic structures. After a disaster, analyzing these structures numerically can be difficult due to their complex geometries, use of multiple construction materials, and alterations to the original structure. This study aimed to digitally document and evaluate the damage caused by the Midwest Tornado in Kentucky in December 2021, specifically focusing on the historic downtown of Mayfield, KY. Building data was gathered using various devices, such as Unmanned Aerial Vehicle’s, LiDARs, and cameras, and converted into finite element meshes using the open-source software Cloud2FEM. Multiple meshes for the historic post office building in Mayfield, KY, was generated using varied rules within Cloud2FEM. These meshes were then simulated using Abaqus to qualitatively assess the stress concentrations observed under tornadic loading calculated using the ASCE7-22. 

Point cloud models of the Mayfield Clothing Mill, a tornado-damaged historic masonry building, are analyzed to understand any possible structural deviations. A point cloud alignment and deviation analysis workflow are described. Cloud2FEM, a point cloud to finite element model (FEM) conversion software, is utilized to generate a FEM for the Clothing Mill. The resulting FEM can be used for structural analysis purposes and be simulated under load conditions to study the structure’s response. 

The process of generating a comprehensive point cloud from the raw data collected at Mayfield involved three distinct steps. Firstly, Pix4D was utilized to process and analyze the data. This was followed by the utilization of Register 360 to further refine and align the collected data. Finally, Cyclone was used to complete the point cloud generation process, ensuring that the resulting point cloud was as detailed and accurate as possible. The combination of these three steps allowed for the creation of a comprehensive point cloud that could be utilized for a variety of applications, ranging from surveying and mapping to construction and design.Damage reconnaissance of historical buildings affected by tornado loading 

Contained within this folder is an exhaustive collection of raw data that was obtained during the on-site data collection process. The data encompasses a range of sources, including aerial imagery, photographs, and LiDAR scans. Each of these sources provides unique information that, when combined, creates a highly detailed representation of the site in question. The aerial imagery captures an overview of the site from a bird's-eye perspective, while the photographs provide a more granular view of specific areas. The LiDAR scans, on the other hand, use laser technology to capture highly accurate data on the contours and elevations of the site. Together, these three sources of data provide a comprehensive understanding of the site that can be utilized for a variety of applications.Damage reconnaissance of historical buildings affected by tornado loading 

Models of 12 historic masonry buildings damaged by an EF4 tornado were created by combining Unmanned Aerial Vehicle Structure-from-Motion (UAV-SfM) and Light Detection and Ranging (LiDAR) point clouds. The building models can be used for a myriad of purposes, such as structural analysis. Additionally, the point cloud combination workflow can be applied to other projects.