Search for: All records

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. 

Observing damage and documenting successful performance of buildings and other structures. Classes include residential, commercial, and power infrastructure. Methodologies include detailed damage assessments in Fulcrum, deployment of UAS for high-resolution aerial imagery, and deployment of surface-level panoramic imaging devices. Hazard indicators were also captured.In the early morning hours of March 3, 2020, a strong tornado struck the City of Nashville and the surrounding metropolitan region with estimated maximum wind speeds of 165 mph. The tornado passed through Nashville and continued east for 53 miles, impacting the communities of Donelson, Mt. Juliet and Lebanon before lifting. The same storm system then produced a second tornado that struck Cookeville, TN with estimated wind speeds of 175 mph. The Nashville tornado was the third tornado that passed through the Five Points area of Nashville. Damage was reported across a diverse cross-section of buildings spanning a number of communities: Camden, Germantown/North Nashville, East Nashville/Five Points, Donelson, Mt. Juliet, Lebanon and Cookeville. Exposure of an urban metro area to this series of tornadoes resulted in significant impacts to power infrastructure and building performance ranging from loss of roof cover and broken windows to complete destruction. Affected typologies and building classes include single and multi-family wood framed homes, commercial construction (ranging from big box stores down to smaller restaurants/retail shops), airport and industrial buildings, and a number of schools. More gravely, these nocturnal tornadoes claimed two dozen lives and injured hundreds more. Given the loss of life and property in this event and the fact that the Nashville tornado sequence impacted an urban area with diverse building classes and typologies, this event offers an opportunity to advance our knowledge of structural resistance to strong winds, particularly given that new construction was among the inventory significantly damaged. This project encompasses the products of StEER's response to this event: Preliminary Virtual Reconnaissance Report (PVRR), Early Access Reconnaissance Report (EARR) and Curated Dataset. 

On September 1 2019, Hurricane Dorian made landfall in Elbow Cay in the Bahamas with sustained winds of 295 km/h and a central pressure of 910 mb, with subsequent landfalls in Marsh Harbour and Grand Bahama Island, where it stalled for two days. This paper presents field observations of Dorian’s coastal hazards and impacts on the built environment in these locales, collected by the Structural Extreme Events Reconnaissance (StEER) Network. Data were collected using a mixed methodological approach: (1) surveying high-water marks and inundation extent, including an approximately 8 m high water mark in Marsh Harbour, (2) conducting surface-level forensic assessments of damage to 358 structures, and (3) rapidly imaging 475 km of routes using street-level panoramas. Field observations are complemented by a debris field analysis using high-resolution satellite imagery. Observed performance reiterates the potential for well-confined, elevated construction to perform well under major hurricanes, but with the need to codify such practices through the addition of storm surge design provisions and an increase in the design wind speeds in the Bahamas Building Code. This study further demonstrates the value of robust reconnaissance infrastructure for capturing perishable data following hurricanes and making such data rapidly available using publicly accessible platforms.