| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | November 1, 2021 |
| Latest Amendment Date: | November 1, 2021 |
| Award Number: | 2053935 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Joy Pauschke
jpauschk@nsf.gov (703)292-7024 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | October 15, 2021 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $399,235.00 |
| Total Awarded Amount to Date: | $399,235.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
321-A INGRAM HALL AUBURN AL US 36849 (334)844-4438 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Auburn AL US 36849-5337 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | DRRG-Disaster Resilience Res G |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Extreme windstorms, including hurricanes, tornadoes, and thunderstorms, are major drivers of economic losses and fatalities in the United States. Mitigating these impacts requires in-depth understanding of the fundamental characteristics of extreme windstorms. These characteristics then inform building codes and standards, education, and risk assessment. Significant knowledge gaps exist for tornadoes and thunderstorms, with basic characteristics such as near-ground wind speeds rarely measured directly. A promising new approach to address these gaps is the application of computer vision techniques to track the 4D motion of wind-borne debris that is contained in the numerous videos of extreme windstorms generated each year by scientists and citizen scientists. This multi-disciplinary Disaster Resilience Research Grants (DRRG) project will integrate wind engineering, structural engineering, computer vision, and machine learning disciplines to develop robust new datasets and methods for understanding near-surface wind and debris characteristics. Graduate students from each discipline will be trained in cross-disciplinary methods. The engagement of citizen scientists will spur awareness and education of the public as to the true nature of these windstorms. Ultimately, the improved understanding of near-ground level winds and debris in extreme windstorms addresses the critical need for improved community resilience to extreme windstorms.
Little is known about the near-surface characteristics of extreme windstorms and the debris they generate. High space and time resolution of velocity fields of these storms are rarely measured in-situ, resulting in fundamental characteristics such as the relative magnitudes of the horizontal and vertical velocity components, vertical profiles of the 3D velocities, and turbulence intensities remaining largely unknown. This project adopts an innovative and integrated approach to characterizing near-surface wind and debris characteristics using visual data sources. The primary objectives of this project are to (1) build a formal database of both structured and unstructured debris motion media with appropriate metadata; (2) generate a robust dataset of labeled debris motion suitable for model training and validation; (3) develop a new generation of computer vision and machine learning based tools with application to fine-scale and large-scale debris identification, classification, and motion tracking; and (4) demonstrate a framework for inferring near-surface wind characteristics from debris motion. In fulfilling these objectives, this project will utilize collaborations with the NHERI Wall of Wind Experimental Facility at Florida International University and citizen scientists within storm chasing networks. The datasets created through this project can be used to train a new generation of tools, integrating artificial intelligence and civil engineering in ways that will ultimately benefit both fields. The outcome will be a deeper understanding of extreme windstorms, with a framework in place for continuous refinement and learning beyond the lifespan of this project.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Extreme windstorms such as hurricanes and tornadoes often loft large debris objects, the motion of which is frequently captured in videos by storm chasers or other members of the general public. This project developed and piloted a framework for back-estimating wind characteristics (e.g., velocity) from the motion of debris in these videos. A comprehensive set of experiments were conducted at the Florida International University Wall of Wind (an Experimental Facility within the National Science Foundation Natural Hazards Engineering Research Infrastructure program) to capture high-quality videos of wind-borne debris motion during controlled conditions where the mass and shape of the debris was known, along with the background wind field. Videos were also solicited from the general public to provide real-world test cases for the framework. The experimental efforts were complemented by the development of a robust digital twin of the framework that was used to evaluate the viability of the framework and quantify the various sources of uncertainty. The findings of the project demonstrated that inferring wind velocity from debris motion can produce accurate results with high-quality videos. The accuracy is sensitive to the accuracy of the estimates of the mass and shape of the debris, however, and metadata such as the camera focal length, approximate distance from the debris, and camera lens distortion parameters, are also necessary.
The project has many benefits to the advancement of science and the well-being of the general public. The extensive experimental dataset of debris flight under known flow conditions will improve our understanding of wind-borne debris flight and thus improve our ability to estimate the risk of, and design to mitigate the risk of, windborne debris in extreme windstorms. The dataset is also useful as training for the development of new computer vision models for improved object detection and tracking and monocular depth estimation. Ultimately, the project helps us better estimate the intensity of winds near the ground in extreme windstorms, particularly for tornadoes, which are rarely sampled directly, and in turn improve our design standards for wind. This project also demonstrates the value of citizen science, as the near-ubiquity of smartphones, doorbell cameras, and other video recording devices frequently capture videos of tornadoes that can be used within this framework to advance our understanding of extreme wind characteristics. The project also supported multiple graduate students in civil engineering while exposing them to modern computer vision techniques.
Last Modified: 02/13/2025
Modified by: David B Roueche
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