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

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
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Multiple Damage Detection of Pipeline Structures Using Statistical Pattern Recognition of Self-sensed Guided Waves

자가 계측 유도 초음파의 통계적 패턴인식을 이용하는 배관 구조물의 복합 손상 진단 기법

  • 박승희 (성균관대학교 사회환경시스템공학과) ;
  • 김동진 (성균관대학교 u-City 공학과) ;
  • 이창길 (성균관대학교 건설환경시스템공학과)
  • Received : 2010.12.17
  • Accepted : 2011.01.16
  • Published : 2011.05.30
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Abstract

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.

최근 사회 기반 시설물에서 구조물의 안전성 및 적정 성능 수준을 확보하기 위하여 구조물의 결함 빛 노후화에 의한 성능 저하 등을 상시적으로 모니터링하기 위한 관심이 높아지고 있다. 이 중 배관 구조물은 국가 주요 자원의 수송을 책임지는 핵심 사회 기반 시설물임에도 불구하고 지중에 매립된다는 위치적 특성 상 상시적으로 구조물의 상태를 모니터링하기는 매우 어렵다. 또한 배관 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능하다. 따라서 본 연구에서는 이러한 복합 손상을 효율적으로 진단하기 위하여 압전센서를 이용한 자가 계측 회로 기반의 유도 초음파 계측 시스템을 복합 손상 진단에 적용하였다. 유도 초음파 자가 계측으로부터 특정 중심 주파수에 해당하는 구조물의 웨이블렛 응답을 계측한다. 복합 손상을 유형별로 분류하기 위하여 유도 초음파 계측으로부터 추출한 특성을 이용하여 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법에 적용한다. 제안된 기법의 적용성 검토를 위하여 배관 구조물에 인위적으로 다중 손상을 생성시켜 시험을 수행하였다.

Keywords

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