Integrated Video Analysis Framework for Vision-Based Comparison Study on Structural Displacement and Tilt Measurements
Publication: Journal of Structural Engineering
Volume 147, Issue 9
Abstract
With the advancement and wide availability of digital video cameras, compounded with the development and enhancement of computer vision methods, vision-based sensing using a video camera as a sensor is promising to become a viable alternative and complementary approach to conventional structural health monitoring (SHM). Among many video analytics methods, no single method is effective for various practical SHM applications using a video camera as a sensor. Not only is it vitally important to understand the effectiveness and shortcomings of the methods, but also there is a need for developing an integrated framework and software tool to enable structural engineers to apply the suitable method for the SHM case at hand. To bridge the gap between the research and practical vision-based SHM applications, this paper presents a unified video analysis framework that integrates both the phase-based algorithm and template matching methods for processing SHM videos recorded using either industrial or consumer types of cameras. The methods have been implemented and integrated into a versatile software tool for extracting structural responses such as displacements, velocities, and accelerations at the points of interest on a structure. The integrated tool enables us to conduct a comparison study of displacement and tilt measurements for a reinforced concrete wall tested in a large structural laboratory and a highway bridge test in the field. The results obtained for the tests have been compared with measurements by conventional sensors. The comparison reveals the advantages and limitations of the phase-based algorithm and the template matching methods. The integrated framework, together with the lessons learned from the comprehensive comparison study, facilitates effective SHM applications using a video camera as a sensor in practice.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party, including the concrete wall test data set and DRBA bridge test dataset. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
Professor Santiago Pujol and Ms. Aishwarya Puranam, from the School of Civil Engineering, Purdue University, have provided the data set for the lab test of the concrete-reinforced wall, which enables us to validate the vision-based method. Their support is gratefully appreciated. The authors would like to thank the Delaware River and Bay Authority for offering the I-295 Delaware bridge for testing and for their tremendous support of the project. They would also like to thank the contractor Mumford and Miller and the many students and support staff from the University of Delaware for their hard work setting up and executing the DRBA bridge test. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not reflect the official views or policies of the State of Delaware or the Federal Highway Administration.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 9 • September 2021
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© 2021 American Society of Civil Engineers.
History
Received: May 15, 2020
Accepted: Apr 14, 2021
Published online: Jul 8, 2021
Published in print: Sep 1, 2021
Discussion open until: Dec 8, 2021
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