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

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
권호 표지
DOI QR코드

DOI QR Code

Compression Sensing Technique for Efficient Structural Health Monitoring - Focusing on Optimization of CAFB and Shaking Table Test Using Kobe Seismic Waveforms

효율적인 SHM을 위한 압축센싱 기술 - Kobe 지진파형을 이용한 CAFB의 최적화 및 지진응답실험 중심으로

  • 허광희 (건양대학교, 해외건설플랜트학과) ;
  • 이진옥 (충남대학교, 토목공학과) ;
  • 서상구 (충남도립대학교, 건설정보학과) ;
  • 정유승 (건양대학교, 재난안전공학과대학원) ;
  • 전준용 (건양대학교, 재난안전공학과대학원)
  • Received : 2019.12.30
  • Accepted : 2020.03.30
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The compression sensing technology, CAFB, was developed to obtain the raw signal of the target structure by compressing it into a signal of the intended frequency range. At this point, for compression sensing, the CAFB can be optimized for various reference signals depending on the desired frequency range of the target structure. In addition, optimized CAFB should be able to efficiently compress the effective structural answers of the target structure even in sudden/dangerous conditions such as earthquakes. In this paper, the targeted frequency range for efficient structural integrity monitoring of relatively flexible structures was set below 10Hz, and the optimization method of CAFB for this purpose and the seismic response performance of CAFB in seismic conditions were evaluated experimentally. To this end, in this paper, CAFB was first optimized using Kobe seismic waveform, and embedded it in its own wireless IDAQ system. In addition, seismic response tests were conducted on two span bridges using Kobe seismic waveform. Finally, using an IDAQ system with built-in CAFB, the seismic response of the two-span bridge was wirelessly obtained, and the compression signal obtained was cross-referenced with the raw signal. From the results of the experiment, the compression signal showed excellent response performance and data compression effects in relation to the raw signal, and CAFB was able to effectively compress and sensitize the effective structural response of the structure even in seismic situations. Finally, in this paper, the optimization method of CAFB was presented to suit the intended frequency range (less than 10Hz), and CAFB proved to be an economical and efficient data compression sensing technology for instrumentation-monitoring of seismic conditions.

압축센싱 기술인 CAFB는 대상 구조물의 원시신호를 목적된 주파수 범위의 신호로 압축하여 획득하도록 개발되었다[27]. 이때 압축센싱을 위해 CAFB는 대상 구조물의 목적된 주파수 범위에 따라 다양한 기준신호로 최적화 될 수 있다. 또한, 최적화된 CAFB는 지진과 같은 돌발/위험상황에서도 대상 구조물의 유효한 구조응답을 효율적으로 압축할 수 있어야 한다. 본 논문에서는 상대적으로 유연한 구조물의 효율적인 구조 건전도 모니터링을 위하여 목적된 주파수 범위를 10Hz 미만으로 설정하고, 이를 위한 CAFB의 최적화 방법과 지진상황에서 CAFB의 지진응답성능을실험적으로 평가하였다. 이를 위해 본 논문에서는, 먼저 Kobe 지진파형을 이용하여 CAFB를 최적화하였고, 이를 자체 개발한 무선 IDAQ 시스템에 임베디드 하였다. 그리고, Kobe 지진파형을 이용하여 2경간 교량에 대한 지진응답실험을 수행하였다. 마지막으로 CAFB가 내장된 IDAQ 시스템을 이용하여 실시간으로 2경간 교량의 지진응답을 무선으로 획득하고, 획득된 압축신호는 원시신호와 상호 비교하였다. 실험의 결과로부터 압축신호는 원시신호와 대비하여 우수한 응답성능과 데이터 압축효과를 보였고, 또한 CAFB는 지진상황에서도 구조물의 유효한 구조응답을 효과적으로 압축센싱할 수 있었다. 최종적으로 본 논문에서는 목적된 주파수 범위(10Hz 미만)에 적합하도록 CAFB의 최적화 방법을 제시하였고, CAFB는 지진상황의 계측-모니터링을 위해 경제적이고 효율적인 데이터 압축센싱 기술임을 증명하였다.

Keywords

References

  1. Sohn, H.; Farrar, R.; Hemez, H.; Czarnecki, J. (2002), A Review of Structural Health Monitoring Literature: 1996-2001. Los Alamos National Laboratory.
  2. Wong, K.Y. (2004), Instrumentation and Health Monitoring of Cable-supported Bridge. Structural Control and Health Monitoring, 11, 91-124. https://doi.org/10.1002/stc.33
  3. Jang, S.; Jo, H.; Mechitov, K.; Rice, J.A.; Sim, S.H.; Jung, H.J.; Yun, C.B.; Spencer, Jr.,B.F.; Agha, G. (2010), Structural Health Monitoring of a Cable-stayed Bridge using Smart Sensor Technology: Deployment and Evaluation. Smart Structures and System, 6, 439-459. https://doi.org/10.12989/sss.2010.6.5_6.439
  4. Na, W.S.; Baek, J. (2017), Impedance-Based Non-Destructive Testing Method Combined with Unmanned Aerial Vehicle for Structural Health Monitoring of Civil Infrastructures. Applied Sciences, 17, 15.
  5. Lee, Y.-J.; Cho, S. (2016), SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating. Sensors, 16, 317 https://doi.org/10.3390/s16030317
  6. Abe, M.; Fujino, Y. (2009), Bridge Monitoring in Japan, Encyclopedia of Structural Health Monitoring. John Wiley and Sons, 5.
  7. Koh, H.M.; Lee, H.S.; Kim, S.; Choo, J.F. (2009), Monitoring of Bridge in Korea, Encyclopedia of Structural Health Monitoring. John Wiley and Sons, 5.
  8. Baptista, F.G.; Budoya, D.E.; Almeida, V.A.D.; Ulson, J.A.C. (2014), An Experimental Study on the Effect of Temperature on Piezoelectric Sensors for Impedance-Based Structural Health Monitoring. Sensors, 14, 1208-1227. https://doi.org/10.3390/s140101208
  9. Salmanpour, M.S.; Khodaei, Z.S.; Aliabadi, M.H. (2017), Impact Damage Localisation with Piezoelectric Sensors under Operational and Environmental Conditions. Sensors, 17, 1178. https://doi.org/10.3390/s17051178
  10. Lamonaca, F.; Sciammarella, P.F.; Scuro, C.; Carni, D.L.; Olivito, R.S. (2018), Synchronization of IoT Layers for Structural Health Monitoring. 2018 Workshop on Metrology for Industry 4.0 and IoT, 8428329, 89-94.
  11. Scuro, C.; Sciammarella, P.F.; Lamonaca, F.; Olivito, R.S.; Carni, D.L. (2018), IoT for structural health monitoring. IEEE Instrumentation and Measurement Magazine, 21, 4-9 and 14. https://doi.org/10.1109/MIM.2018.8573586
  12. Aktan, A.E.; Catbas, F.N.; Grimmelsman, K.A.; Pervizpour, M. (2003), Development of a Model Health Monitoring Guide for Major Bridges. Drexel Intelligent Infrastructure and Transportation Safety Institute.
  13. Peeters, B.; Couvreur, G.; Razinkov, O.; Kundig, C. (2003), Continuous Monitoring of the Oresund Bridge: System and Data Analysis. In Proceedings of IMAC 21, International Modal Analysis Conference, Kissimmee, Florida, USA.
  14. Ko, J.M.; Ni, Y.Q. (2005), Technology Developments in Structural Health Monitoring of Large-scale Bridges. Engineering Structures, 27, 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  15. Ko, J.M.; Ni, Y.Q. (2003), Structural Health Monitoring and Intelligent Vibration Control of Cable-Supported Bridge: Research and Application. KSEC Journal of Civil Engineering, 7, 701-716. https://doi.org/10.1007/BF02829139
  16. Carnì, D.L.; Scuro, C.; Lamonaca, F.; Olivito, R.S.; Grimaldi, D. (2017), Damage analysis of concrete structures by means of acoustic emissions technique. Composites Part B: Engineering, 115, 79-86. https://doi.org/10.1016/j.compositesb.2016.10.031
  17. Lamonaca, F.; Carrozzini, A.; Grimaldi, D.; Olivito, R.S. (2015), Improved monitoring of acoustic emissions in concrete structures by multi-triggering and adaptive acquisition time interval. Measurement, 59, 227-236. https://doi.org/10.1016/j.measurement.2014.09.053
  18. Lamonaca, F.; Carrozzini, A.; Grimaldi, D.; Olivito, R.S. (2014), Improved accuracy of damage index evaluation in concrete structures by simultaneous hardware triggering. Metrology and Measurement Systems, 21, 341-350. https://doi.org/10.2478/mms-2014-0029
  19. Heo, G.; Jeon, J. (2009), A Smart Monitoring System Based on Ubiquitous Computing Technique for Infra-structural System: Centering on Identification of Dynamic Characteristics of Self-Anchored Suspension Bridge. KSCE J ournal o f Civil Engineering, 13, 333-337. https://doi.org/10.1007/s12205-009-0333-z
  20. Lynch, P.J. (2007), An Overview of Wireless Structural Health Monitoring for Civil Structures. Philosophical Transactions of the Royal Society A, 365, 345-372. https://doi.org/10.1098/rsta.2006.1932
  21. Zhang, J.; Tian, G.Y.; Marindra, A.M.J.; Sunny, A.I.; Zhao, A.B. (2017), A Review of Passive FRID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications. Sensors, 17, 265. https://doi.org/10.3390/s17020265
  22. Park, J.-W.; Sim, S.-H.; Jung, H.-J.; Spencer, B.F. (2013), Development of a Wireless Displacement Measurement System Using Acceleration Responses. Sensors, 13, 8377-8392. https://doi.org/10.3390/s130708377
  23. Hao, J.; Zhang, B.; Jiao, Z.; Mao, S. (2015), Adaptive compressive sensing based sample scheduling mechanism for wireless sensor networks. Pervasive and Mobile Computing, 22, 113-125. https://doi.org/10.1016/j.pmcj.2015.02.002
  24. Huang, Y.; Beck, J.L.; Wu, S.; Li, H. (2016), Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery. Probabilistic Engineering Mechanics, 46, 62-79. https://doi.org/10.1016/j.probengmech.2016.08.001
  25. Peckens, C.A.; Lynch, J.P. (2013), Utilizing the Cochlea as a Bio-inspired Compressive Sensing Technique. Smart Materials and Structures, 22, 105027. https://doi.org/10.1088/0964-1726/22/10/105027
  26. Peckens, C.A.; Lynch, J.P.; Heo, G. (2015), Resource-efficient Wireless Sensor Network Architecture Based on Bio-mimicry of the Mammalian auditory System. Intelligent Material Systems and Structures, 26, 79-100. https://doi.org/10.1177/1045389X14521877
  27. Heo, G.; Jeon, J. (2017), A Study on the Data Compression Technology-based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of Civil Structures. Sensors, 17, 1620. https://doi.org/10.3390/s17071620
  28. Angrisani, L.; Schiano Lo Moriello, R.; Bonavolonta, F.; Gallucci, L.; Menna, C.; Asprone, D.; Fabbrocino, F. (2017), An innovative embedded wireless sensor network system for the structural health monitoring of RC structures. 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry(RTSI), Conference Proceedings, 8065969.
  29. Gallucci, L.; Menna, C.; Angrisani, L.; Asprone, D.; Lo Moriello, R.S.; Bonavolonta, F.; Fabbrocino, F. (2017), An embedded wireless sensor network with wireless power transmission capability for the structural health monitoring of reinforced concrete structures. Sensors, 17, 2566. https://doi.org/10.3390/s17112566