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지진 재해 대응을 위한 진동 기반 구조적 관로 상태 감시 시스템에 대한 고찰 KCI 등재

A review on vibration-based structural pipeline health monitoring method for seismic response

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  • URLhttps://db.koreascholar.com/Article/Detail/410516
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상하수도학회지 (Journal of the Korean Society of Water and Wastewater)
대한상하수도학회 (Korean Society Of Water And Wastewater)
초록

As the frequency of seismic disasters in Korea has increased rapidly since 2016, interest in systematic maintenance and crisis response technologies for structures has been increasing. A data-based leading management system of Lifeline facilities is important for rapid disaster response. In particular, the water supply network, one of the major Lifeline facilities, must be operated by a systematic maintenance and emergency response system for stable water supply. As one of the methods for this, the importance of the structural health monitoring(SHM) technology has emerged as the recent continuous development of sensor and signal processing technology. Among the various types of SHM, because all machines generate vibration, research and application on the efficiency of a vibration-based SHM are expanding. This paper reviews a vibration-based pipeline SHM system for seismic disaster response of water supply pipelines including types of vibration sensors, the current status of vibration signal processing technology and domestic major research on structural pipeline health monitoring, additionally with application plan for existing pipeline operation system.

목차
ABSTRACT
1. 서 론
2. 진동 기반 구조적 상태 감시 시스템
    2.1 구조적 상태 감시 시스템(Structural healthmonitoring, SHM)
    2.2 진동 기반 구조적 상태 감시 시스템(VibrationbasedSHM)
3. 진동 센서
    3.1 변위계
    3.2 속도계
    3.3 가속도계
4. 진동 신호 처리 기술
    4.1 시계열 분석 모델(Statistical Time Series Models)
    4.2 고속 푸리에 변환(Fast Fourier Transform, FFT)
    4.3 단시간 푸리에 변환(Short-Time Fourier Transform,STFT)
    4.4 위그너 빌 분포(Wigner-Ville distribution, WVD)
    4.5 웨이블릿 변환(Wavelet Transform, WT)
    4.6 칼만 필터(Kalman Filter, KF)
    4.7 서포트 벡터 머신(Support vector machine, SVM)
    4.8 합성곱 신경망(Convolutional Neural Network,CNN)
5. 연구 현황
    5.1 국내 연구 현황
    5.2 국외 연구 현황
6. 적용 방안
7. 결론 및 고찰
References
저자
  • 신동협(고려대학교 건축사회환경공학과) | Dong-Hyup Shin (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 이정훈(고려대학교 건축사회환경공학과) | Jeung-Hoon Lee (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 장용선(고려대학교 건축사회환경공학과) | Yongsun Jang (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 정동휘(고려대학교 건축사회환경공학과) | Donghwi Jung (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 박희등(고려대학교 건축사회환경공학과) | Hee-Deung Park (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 안창훈(고려대학교 건축사회환경공학과) | Chang-Hoon Ahn (School of Civil, Environmental and Architectural Engineering, Korea University) Corresponding author
  • 변역근((주)삼안 상하수도1부) | Yuck-Kun Byun (Water supply & Sewerage Dept.1, Saman Corporation)
  • 김영준((주)삼안 상하수도1부) | Young-Jun Kim (Water supply & Sewerage Dept.1, Saman Corporation)