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주행 하중에 의한 교량 변위 계측에서 영상 변위와 가속도 이중 적분의 조합에 관한 연구

A Study on the Combination of Vision-Based Displacement and Double Integration of Acceleration in Displacement Measurement of Bridge under Moving Load

  • 이형진 (창원대학교 공과대학 스마트 그린공학부 건설시스템공학전공)
  • Hyeong-Jin Lee (Dept. of Civil Engineering, School of Smart & Green Engineering, Changwon National University)
  • 투고 : 2024.08.12
  • 심사 : 2024.08.28
  • 발행 : 2024.10.31

초록

Recently, vision-based displacement(VBD) has been attracting attention as a convenient and economical method. VBD has the advantage of being easy to install and operate, but it also has the disadvantage of being difficult to obtain results that reflect high-frequency characteristics well. On the other hand, indirect displacement measurement through double integration of acceleration may in some cases result in distortion in the low-frequency region, making integration itself impossible. Therefore, neither can be a suitable method alone for measuring the dynamic displacement of bridges under moving loads. In this context, this paper studies how to obtain more accurate dynamic displacement responses of bridges over the entire frequency range by combining two methods, VBD and acceleration integration. The practical applicability was examined through simulation and model experiments. The simulation results show that both basic methods have fundamental limitations in each frequency range, while the combined method can be an good alternative to overcome these limitations. The applicability of the combination method through model experiments also showed relatively good results, but the results were inferior to those in the theoretical case. The reason is that errors in each basic measurement methods are transferred to the combined method. In other words, it is important that the basic measurement method also secures sufficient accuracy at least in each advantageous frequency band. In conclusion, combining the strengths of different methods to create a better alternative is always a good approach, and the method in this paper is meaningful in that it demonstrates one such alternative.

키워드

과제정보

이 논문은 2023~2024년도 창원대학교 자율연구과제로 수행되었음.

참고문헌

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