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http://dx.doi.org/10.7734/COSEIK.2021.34.4.183

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

Shin, Yoon-Soo (Division of Architectural Engineering, DanKook University)
Min, Kyung-Won (Division of Architectural Engineering, DanKook University)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.34, no.4, 2021 , pp. 183-189 More about this Journal
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.
Keywords
ensemble learning; raspberry pi; sensor; LoRa; structural health monitoring; distributed computing;
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  • Reference
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.   DOI
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.   DOI
5 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.   DOI
6 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.   DOI
7 Michal, V., Milan, S., Rudolf, A. (2018) Ambient Vibration Measurements of Steel Truss Bridges, J. Meas. Eng., 6, pp.234~239.   DOI
8 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.   DOI
9 Velez, F.J., Nadziejko, A. (2015) Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones, 2015 IEEE 82nd Vehicular, Technology Conference.
10 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.
11 Narayan, P.P., Minsakshee, M.P. (2014) Driver Assistance System based on Raspberry Pi, Int. J. Comput. Appl., 95(16), pp.36~39.   DOI
12 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.