Automated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy
Publication: Journal of Computing in Civil Engineering
Volume 35, Issue 6
Abstract
Digital image processing is considered an alternative to manual visual inspection, enabling automated damage evaluation for structural maintenance. Although advancements in artificial intelligence have improved identification performance, directly quantifying the surface damage in three-dimensional (3D) space using only two-dimensional (2D) images is difficult. In addition, because close-up images are preferred owing to the high measurement accuracy, its application requires a considerable amount of time to process numerous images of full-scale structure. In this study, a framework for automated damage evaluation using 3D laser scanning is presented. The proposed approach is designed to process the point clouds of a full-scale bridge by addressing different shapes. Furthermore, a tailored fitting strategy is employed to accurately identify the surface damage on the edge, which can cause false detections. In practice, the performance of the proposed framework is systematically validated on the point clouds of the bridge components.
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Data Availability Statement
All data, models, and codes generated or used during the study are available in part or in full from the corresponding author upon request.
Acknowledgments
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A3A03039383).
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Published In
Journal of Computing in Civil Engineering
Volume 35 • Issue 6 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 18, 2021
Accepted: Jul 28, 2021
Published online: Sep 9, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 9, 2022
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