Journal of the Korea institute for structural maintenance and inspection (한국구조물진단유지관리공학회 논문집)

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
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Development of Damage Evaluation Technology Considering Variability for Cable Damage Detection of Cable-Stayed Bridges

사장교의 케이블 손상 검출을 위한 변동성이 고려된 손상평가 기술 개발

  • 고병찬 (건양대학원 재난안전공학과) ;
  • 허광희 (건양대학교 해외건설플랜트학과) ;
  • 박채린 (건양대학원 재난안전공학과) ;
  • 서영득 (지진방재연구센터) ;
  • 김충길 (건양대학교 공공안전연구소)
  • Received : 2020.10.15
  • Accepted : 2020.11.09
  • Published : 2020.12.31
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Abstract

In this paper, we developed a damage evaluation technique that can determine the damage location of a long-sized structure such as a cable-stayed bridge, and verified the performance of the developed technique through experiments. The damage assessment method aims to extract data that can evaluate the damage of the structure without the undamage data and can determine the damage location only by analyzing the response data of the structure. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To evaluate the performance of the developed technique experimentally, cable damage experiments were conducted on model cable-stayed bridges. As a result, the damage assessment method considering variability automatically outputs the damageless data according to external force, and it is confirmed that the performance of extracting information that can determine the damage location of the cable through the analysis of the outputted damageless data and the measured damage data is shown.

본 논문에서는 사장교와 같은 장대형 구조물의 손상위치를 판단할 수 있는 손상평가 기법을 개발하고, 개발한 기법의 성능을 실험을 통하여 검증하고자 하였다. 손상평가 기법은 무손상 데이터가 확보되지 않은 상태에서 구조물의 손상평가가 가능하고, 구조물의 응답 데이터의 분석만으로 손상위치를 판단할 수 있는 데이터를 추출하는 것을 목표로 하였다. 이러한 목표를 완성하기 위하여, 손상 위치 판별을 위하여 통계적 패턴인식 기술인 개선된 마할라노비스 거리(IMD : Improved Mahalanobis Distance) 이론에 기반하여 변동성이 고려된 손상평가 기법을 개발하였다. 개발한 손상평가 기법에는 구조물의 고유한 정보에 기반한 Simulation 프로그램을 반영하여 다양한 외력에 따른 구조물의 무손상 응답을 출력하도록 하였다. 개발한 기법의 성능을 실험적으로 평가하기 위하여 모형 사장교를 대상으로 케이블 손상실험을 수행하였다. 그 결과, 변동성이 고려된 손상평가 기법은 외력에 따른 무손상 데이터를 자동으로 출력하고, 출력된 무손상 데이터와 계측된 손상 데이터의 분석을 통하여 케이블의 손상 위치를 판단할 수 있는 정보를 추출하는 성능을 보이는 것을 확인하였다.

Keywords

References

  1. Li, H., & Ou, J. (2016). The state of the art in structural health monitoring of cable-stayed bridges. Journal of Civil Structural Health Monitoring, 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x
  2. Amezquita-Sanchez, J. P., & Adeli, H. (2014). Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures. Archives of Computational Methods in Engineering, 23(1), 1-15. https://doi.org/10.1007/s11831-014-9135-7
  3. Meruane, V., & Heylen, W. (2011). An hybrid real genetic algorithm to detect structural damage using modal properties. Mechanical Systems and Signal Processing, 25(5), 1559-1573. https://doi.org/10.1016/j.ymssp.2010.11.020
  4. Lederman, G., Wang, Z., Bielak, J., Noh, H., Garrett, J. H., Chen, S., ... & Rizzo, P. (2014, July). Damage quantification and localization algorithms for indirect SHM of bridges. In Proc. Int. Conf. Bridge Maint., Safety Manag., Shanghai, China
  5. Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154-170. https://doi.org/10.1016/j.jsv.2016.10.043
  6. Teng, Z., Teng, S., Zhang, J., Chen, G., & Cui, F. (2020). Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network. Applied Sciences, 10(14), 4720. https://doi.org/10.3390/app10144720
  7. Farahani, R. V., & Penumadu, D. (2016). Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data. Engineering Structures, 115, 129-139. https://doi.org/10.1016/j.engstruct.2016.02.008
  8. Dohler, M., Hille, F., Mevel, L., & Rucker, W. (2014). Structural health monitoring with statistical methods during progressive damage test of S101 Bridge. Engineering Structures, 69, 183-193. https://doi.org/10.1016/j.engstruct.2014.03.010
  9. Li, Z., Park, H. S., & Adeli, H. (2017). New method for modal identification of super high‐rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms. The Structural Design of Tall and Special Buildings, 26(3), e1312. https://doi.org/10.1002/tal.1312
  10. Fugate, M. L., Sohn, H., & Farrar, C. R. (2001). Vibration-based damage detection using statistical process control. Mechanical Systems and Signal Processing, 15(4), 707-721. https://doi.org/10.1006/mssp.2000.1323
  11. Worden, K., Manson, G., & Fieller, N. R. (2000). Damage detection using outlier analysis. Journal of Sound and Vibration, 229(3), 647-667. https://doi.org/10.1006/jsvi.1999.2514
  12. Wang, L., Wu, Y., & Wang, D. (2014). Thermal effect on damaged stay-cables. Journal of Theoretical and Applied Mechanics, 52(4), 1071-1082. https://doi.org/10.15632/jtam-pl.52.4.1071
  13. Li, H., & Ou, J. (2016). The state of the art in structural health monitoring of cable-stayed bridges. Journal of Civil Structural Health Monitoring, 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x
  14. Heo, G., Kim. (2014). New Statistical Pattern Recognition Technology for Condition Assessment of Cable-stayed Bridges on Earthquake Load. KSCE Journal of Civil Engineering, 34(3), 747-754.
  15. Heo, G., Wang, M. L., & Satpathi, D. (1997). Optimal transducer placement for health monitoring of long span bridge. Soil Dynamics and Earthquake Engineering, 16(7-8), 495-502. https://doi.org/10.1016/S0267-7261(97)00010-9