DOI QR코드

DOI QR Code

Cable Tension Measurement of Long-span Bridges Using Vision-based System

영상처리기법을 이용한 장대교량 케이블의 장력 측정

  • 김성완 (부산대학교 지진방재연구센터) ;
  • 정진환 (부산대학교 건설융합학부) ;
  • 김성도 (경성대학교 토목공학과)
  • Received : 2017.11.22
  • Accepted : 2018.02.05
  • Published : 2018.03.01

Abstract

In a long-span bridge, the cables are important elements that support the load of the bridge. Accordingly, the cable tension is a very important variable in evaluating the health and safety of the bridge. The most popular methods of estimating the cable tensions are the direct method, which directly measures the cable stresses using load cells, hydraulic jacking devices, etc., and the vibration method, which inverses the tensions using the cable shapes and the measured dynamic characteristics. Studies on the use of the electromagnetic (EM) sensor, which detects the magnetic field variations caused by the change in the stress of the steel in the cable, are increasing. In this study, the lift-off test, the EM sensor, and the vibration method (Vision-based System and Accelerometer) were used to measure cable tension, and their results were compared and analyzed.

케이블지지교량에서 케이블은 하중을 지지하는 주요 부재로, 케이블 장력은 교량의 건전성과 안전도 평가에 있어서 매우 중요한 변수이다. 케이블의 장력을 추정하는 기법으로, 로드셀 및 유압잭 등을 이용하여 케이블의 응력을 직접 측정하는 직접법과 케이블의 형상조건과 계측된 동특성을 활용하여 장력을 역산하는 진동법이 가장 많이 활용되고 있다. 최근 들어 케이블 내부 강재의 응력변화로 인하여 유발되는 자기장 변화를 탐지하는 EM 센서의 연구 및 활용이 증가하고 있다. 본 연구에서는 리프트오프 테스트, EM 센서 및 진동법(Vision-based System, Accelerometer)을 적용하여 장력을 측정하고 그 결과를 비교 분석하였다.

Keywords

References

  1. Chen, J., Xia, G., Zhou, K., Xia, G., and Qin, Y. (2005), Two-step digital image correlation for micro-region measurement, Optics Lasers in Engineering, 43(8), 836-846. https://doi.org/10.1016/j.optlaseng.2004.09.002
  2. Cho, S. J., Lynch, J. P., Lee, J. J., and Yun, C. B. (2010), Development of an automated wireless tension force estimation system for cablestayed bridges, Journal of Intelligent Material Systems and Structures, 21, 361-376. https://doi.org/10.1177/1045389X09350719
  3. Duan, Y. F., Zhang, R., Zhao, Y. S., Or, W., Fan, K. Q., and Tang, Z. F. (2011), Smart elasto-magneto-electric (EME) sensors for stress monitoring of steel structures in railway infrastructures, Journal of Zhejiang University SCIENCE A, 12(2), 895-901. https://doi.org/10.1631/jzus.A11GT007
  4. Hild, F., and Roux, S. (2012), Comparison of local and global approaches to digital image correlation, Experimental Mechanics, 52(1), 1503-1519. https://doi.org/10.1007/s11340-012-9603-7
  5. Kim, J. T., Huynh, T. C., and Lee, S. Y. (2014), Wireless structural health monitoring of stay cables under two consecutive typhoons, Structural Monitoring and Maintenance, 1(1), 047-067. https://doi.org/10.12989/smm.2014.1.1.047
  6. Kim, S. W., and Kim, N. S. (2013), Dynamic characteristics of suspension bridge hanger cables using digital image processing, NDT&E International, 59, 25-33. https://doi.org/10.1016/j.ndteint.2013.05.002
  7. Kim, S. W., Jeon, B. G., Cheung, J. H., Kim, S. D., and Park, J. B. (2017), Stay cable tension estimation using a vision-based monitoring system under various weather conditions, Civil Structural Health Monitoring, 7, 343-357. https://doi.org/10.1007/s13349-017-0226-7
  8. Kim, S. W., Jeon, B. G., Kim, N. S., and Park, J. C. (2013), Vision-based monitoring system for evaluating cable tensile forces on a cablestayed bridge, Structural Health Monitoring, 12(5-6), 440-456. https://doi.org/10.1177/1475921713500513
  9. Nguyen, K. D., Kim, J. T., and Park, Y. H. (2013), Long-term vibration monitoring of cable-stayed bridge using wireless sensor network, International Journal of Distributed Sensor Networks, 1-9.
  10. Pan, B., Qian, K., Xie, H., and Asundi, A. (2009), Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Measurement Science Technology, 20, 1-17.
  11. Shimada, T. (2000), Estimating Method of Cable Tension form Natural Frequency of High Mode, Proceeding of JSCE, 501(1-29), 163-171.
  12. Zitova, B., and Flusser, J. (2013), Image registration methods: a survey Image, Image and Vision Computing, 21(11), 977-1000. https://doi.org/10.1016/S0262-8856(03)00137-9