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논문 기본 정보

자료유형
학술저널
저자정보
Hyung-Jo Jung (Korean Advanced Institute for Science and Technolo)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.24 No.5
발행연도
2019.1
수록면
669 - 681 (13page)

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Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV’s flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).
#bridge inspection #unmanned aerial vehicle (UAV) #imaging device #condition assessment #deep learning algorithm

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참고문헌 (45)

참고문헌 신청
Muhammad Ali Akbar / 2019 / An evaluation of image-based structural health monitoring using integrated unmanned aerial vehicle platform / Structural Control and Health Monitoring 26(1):e2276 Crossref American Society for Civil Engineers (ASCE) google schola Brooks, C. / Evaluating the use of unmanned aerial vehicles for transportation purposes (No. RC-1616) google schola Brodie Chan / 2015 / Towards UAV-based bridge inspection systems: a review and an application perspective / Structural Monitoring and Maintenance 2(3):283~300 Crossref Chen, S. / 2018 / Automated bridge deck evaluation through UAV derived point cloud / Proceedings of the 2018 Civil Engineering Research in Ireland Conference google schola

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