• Title/Summary/Keyword: 피로균열진전실험

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Multi-fidelity Data-fusion for Improving Strain accuracy using Optical Fiber Sensors (이종 광섬유 센서 데이터 융합을 통한 변형률 정확도 향상 기법)

  • Park, Young-Soo;Jin, Seung-Seop;Yoo, Chul-Hwan;Kim, Sungtae;Park, Young-Hwan
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.40 no.6
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    • pp.547-553
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    • 2020
  • As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain.