지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.24 No.5
- 발행연도
- 2019.1
- 수록면
- 617 - 630 (14page)
이용수
초록· 키워드
오류제보하기
Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.
목차
등록된 정보가 없습니다.
참고문헌 (60)
참고문헌 신청
Canny, J. F. / 1986 / A computational approach to edge detection / IEEE T. Pattern Anal. Mach. Intell 8(6):679~698
F. Necati Catbas / 2012 / Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating / Journal of Bridge Engineering 17(4):662~670
Chen, S. -E. / 2012 / 3D liDAR scans for bridge damage evaluations / Sixth Congr. Forensic Eng., San Francisco, California, United States :487~495
Catbas, F. N. / 2017 / Comprehensive Structural Health Monitoring at Local and Global Level with Vision-based Technologies / 26th ASNT Res. Symp
Catbas, F. N. / 2018 / A vision for vision-based technologies for bridge health monitoring / Maintenance, Safety, Risk, Manag. Life-Cycle Perform. Bridg. Proc. Ninth Int. Conf. Bridg. Maintenance, Saf. Manag. (IABMAS 2018) :54
F. Necati Catbas / 2012 / Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating / Journal of Bridge Engineering 17(4):662~670
Chen, S. -E. / 2012 / 3D liDAR scans for bridge damage evaluations / Sixth Congr. Forensic Eng., San Francisco, California, United States :487~495
Catbas, F. N. / 2017 / Comprehensive Structural Health Monitoring at Local and Global Level with Vision-based Technologies / 26th ASNT Res. Symp
Catbas, F. N. / 2018 / A vision for vision-based technologies for bridge health monitoring / Maintenance, Safety, Risk, Manag. Life-Cycle Perform. Bridg. Proc. Ninth Int. Conf. Bridg. Maintenance, Saf. Manag. (IABMAS 2018) :54
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