Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
권호 표지
DOI QR코드

DOI QR Code

Decentralized Structural Diagnosis and Monitoring System for Ensemble Learning on Dynamic Characteristics

동특성 앙상블 학습 기반 구조물 진단 모니터링 분산처리 시스템

  • Shin, Yoon-Soo (Division of Architectural Engineering, DanKook University) ;
  • Min, Kyung-Won (Division of Architectural Engineering, DanKook University)
  • 신윤수 (단국대학교 건축공학과) ;
  • 민경원 (단국대학교 건축공학과)
  • Received : 2021.05.13
  • Accepted : 2021.07.08
  • Published : 2021.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

구조물에 장기적으로 발생하는 노후화를 정량적으로 파악하기 위해 상시진동 데이터를 활용한 일반화된 모니터링 시스템에 관한 연구가 세계적으로 활발히 수행중이다. 본 연구에서는 구조물에서 장기적으로 취득되는 동특성을 앙상블 학습에 활용하여 구조물의 이상을 감지하기 위한 보급형 엣지 컴퓨팅 시스템을 구축하였다. 시스템의 하드웨어는 라즈베리파이와 보급형 가속도계, 기울기센서, GPS RTK 모듈, 로라 모듈로 구성됐다. 실험실 규모의 구조물 모형 진동실험을 통해 동특성을 활용한 앙상블 학습의 구조물 이상감지를 검증하였으며, 실험을 기반으로 한 실시간 동특성 추출 분산처리 알고리즘을 라즈베리파이에 탑재하였다. 구축된 시스템을 하우징하고 포항시 행정복지센터에 설치하여 데이터를 취득함으로써 개발된 시스템의 현장 적용성을 검증하였다.

Keywords

Acknowledgement

본 연구는 2020년도 단국대학교의 대학연구비의 지원으로 연구되었음.

References

  1. Buhus, E.R., Dimis, D., Apatean, A. (2016) Automatic Parking Access using Openalpr on Raspberry Pi3, Electron. & Telecommun., 57(3). pp.10~15.
  2. Cyrel, O.M., Jesus, M.M., Jackson, L.B., Czarleine, K.P., Maria, K.T. (2015) Real-Time Integrated CCTV Using Face and Pedestrian Detection Image Processing Algorithm For Automatic Traffic Light Transitions, 8th IEEE International Conference Humanoid, 2015.
  3. Ferdoush, S., Li, X. (2014) Wireless Sensor Network System Design using Raspberry Pi and Arduino for Environmental Monitoring Applications, Proc. Comput. Sci., 34(2014), pp.103~110. https://doi.org/10.1016/j.procs.2014.07.059
  4. Hao, S., Aurelien, M., German, A., Prieto, M., Nafi T., Oral, B. (2017) Bayesian Characterization of Buildings using Seismic Interferometry on Ambient Vibrations, Mech. Syst. & Signal Process., 85 pp.468~486. https://doi.org/10.1016/j.ymssp.2016.08.038
  5. Jang, S.H., Kim, J.S., Lim, K.H., Sung, J.Y., Park, K.J. (2011) Utilization Plan and Characteristic of Seismic Acceleration Response Signal, National Institute for Disaster Prevention.
  6. Kim, J.H., Lynch, J.P. (2012) Subspace System Identification of Support Excited Structures Part II: Gray-Box Interpretations and Damage Detection, Earthq. Eng. & Struct. Dyn., 41(15), pp.2253~2271. https://doi.org/10.1002/eqe.2185
  7. Kim, J.H., Sohn, H. (2013) Data-Driven Physical Parameter Estimation for Lumped Mass Structures from a Single Point Actuation Test, J. Sound & Vib., 332(18), pp.4390~4402. https://doi.org/10.1016/j.jsv.2013.03.006
  8. Michal, V., Milan, S., Rudolf, A. (2018) Ambient Vibration Measurements of Steel Truss Bridges, J. Meas. Eng., 6, pp.234~239. https://doi.org/10.21595/jme.2018.20419
  9. Min, K.W., Kim, J.H., Park, S.A., Park, C.S. (2013) Ambient Vibration Testing for Story Stiffness Estimation of a Heritage Timber Building, The Sci. World J., 2013.
  10. Narayan, P.P., Minsakshee, M.P. (2014) Driver Assistance System based on Raspberry Pi, Int. J. Comput. Appl., 95(16), pp.36~39. https://doi.org/10.5120/16682-6794
  11. Shin, Y.S., Kim, J.H., Min, K.W. (2019) Design of Edge Computing System Utilizing Raspberry Pi for Structural Response Measurement, J. Comput. Struct. Eng. Inst. Korea, 32(6), pp.375~381. https://doi.org/10.7734/COSEIK.2019.32.6.375
  12. Velez, F.J., Nadziejko, A. (2015) Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones, 2015 IEEE 82nd Vehicular, Technology Conference.