Structural Dynamics Identification Using Motion Estimation and Video Magnification

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This project aims to explore the use of a non-contact, video camera-based method to identify small motions and dynamic behavior of structures. This has the potential to help engineers understand complex dynamic behavior of 3D structures, including, for example, wind turbines, bridges, aircraft, ships, and automobiles. This will help in structural health monitoring, as well as in testing numerical model predictions of deformations of large, complex structures. Videos can be recorded, and deformations and dynamic behavior distilled using a portable electronic device application (app) developed for a cell phone. This will permit simplified use of the technology by researchers, design engineers and field inspectors. The research team will work with high school students to integrate the video sensing technology into their curriculum. The project will also involve female and minority students.The primary research objective of this project is to understand and quantify the relationship between the structural dynamics extracted via camera video motion magnification and the true dynamic motion. The project has the potential to transform the way large-scale experimental modal analysis and three-dimensional (3D) structural dynamics identification is currently performed. The successful completion of the work will lead to a better understanding of vision-based structural dynamic parameter extraction and how the processed data is affected by environmental and operational variabilities. This work will integrate the phase-based motion estimation technique with 3D digital image correlation and point tracking to achieve an understanding of how two-dimensional camera measurements can be used to extract quantitative 3D motion. The successful outcomes of this project will address the existing knowledge gap between the factors that contaminate the related algorithms and the processed results, while quantifying uncertainty for video-based structural dynamics identification. The research will generate a systematic understanding of how extracted optical flows are associated with dynamic motions. Achieving a quantitative understanding of how different parameters influence the results for phase-based motion extraction and video magnification will lead to a transformative new approach for non-contact measurement that can impact system identification, validation, and structural health monitoring of various infrastructure systems.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.