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Damage visualization of pipeline structures using laser-induced ultrasonic waves
Lee, Changgil,Park, Seunghee SAGE Publications 2015 Structural health monitoring Vol.14 No.5
<P>In this study, a noncontact non-destructive testing method is implemented to visualize damages of a pipeline structure. An ND:YAG pulsed laser system was used to generate guided waves and a galvanometer-based laser scanner scans a specific area to find damage location. The wave responses were measured using a piezoelectric sensor attached to the pipeline. The measured time and spatial responses were transformed to data in frequency and wavenumber domains through three-dimensional Fourier transform. Damage-sensitive features could be obtained using wavenumber filter to extract standing wave energy. A flaw imaging technique of the pipeline structure was conducted by calculating root mean square. Notches and thickness reduction at a pipeline structure were investigated to verify the effectiveness and the robustness of the proposed non-destructive testing approach. Additionally, a series of experiments were repeated under heating condition to consider the real-world pipeline structures.</P>
Lee, Changgil,Park, Seunghee,Bolander, John E.,Pyo, Sukhoon Elsevier BV 2018 Construction and Building Materials Vol.163 No.-
<P><B>Abstract</B></P> <P>In this study, the hardening process of ultra high performance concrete (UHPC) was monitored non-destructively using a single embedded sensor system and the characteristics of guided waves, especially the Lamb wave. Lamb wave propagation depends on the material properties of the medium and boundary conditions. Since the boundary conditions of the embedded sensor system continuously change during the hardening process of concrete materials, the measured characteristics of the propagating waves also vary. To understand the variations in wave propagation, the Lamb modes were decomposed using the polarization characteristics of piezoelectric sensors, which were used to measure wave responses. Additionally, a traditional penetration resistance method was adopted to estimate the time for phase transition of UHPC. The decomposed Lamb modes were compared to measurements of penetration resistance. The strength development of UHPC, with and without short-fiber reinforcement, was estimated using the variation of patterns of the decomposed Lamb modes after the phase transition. Based on the proposed methodology, which measures the propagation and variation of the Lamb waves, it is possible to estimate the time of phase transition and the strength development of UHPC.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Single embedded sensor is used to monitor early and longer-term hardening process of UHPC. </LI> <LI> Features of the hardening process are extracted from single Lamb wave mode. </LI> <LI> Attenuation and wave velocity of the Lamb mode serve as bases for strength estimation. </LI> <LI> The effects of carbon nanotube (CNT) and steel fiber additions are demonstrated. </LI> </UL> </P>
자율 감지 및 확률론적 신경망 기반 패턴 인식을 이용한 배관 구조물 손상 진단 기법
이창길(Changgil Lee),박웅기(Woong-Ki Park),박승희(Seunghee Park) 한국비파괴검사학회 2011 한국비파괴검사학회지 Vol.31 No.4
최근 토목, 기계 및 항공 분야에서 구조물의 안전성 및 적정 성능 수준 확보를 위하여 구조물의 결함 및 노후화에 의한 성능저하 등을 상시적으로 모니터링하기 위한 관심이 높아지고 있다. 실제 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능한데, 복합 손상을 단일 모드 계측 시스템으로부터 진단하기는 매우 어렵다. 따라서 본 연구에서는 이러한 복합 손상을 효율적으로 진단하기 위하여 선행 연구에서 제안된 압전센서를 이용한 자가 계측 회로 기반의 다중 모드 계측 시스템을 적용하였다. 자가 계측 회로 기반 다중 모드 계측 시스템은 크게 두 가지 형태의 신호를 계측한다. 첫 번째 모드는 임피던스 계측으로부터 특정 주파수 대역의 구조 응답을 계측하며, 두 번째 모드는 유도 초음파 계측으로부터 단일 중심 주파수에 해당하는 구조 응답을 계측한다. 복합 손상을 손상 유형별로 분류하기 위하여 E/M 임피던스와 유도 초음파의 계측으로부터 추출한 특성을 이용하여 2차원 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법 중 확률론적 신경망 기법에 적용한다. 제안된 기법의 적용성 검토를 배관 구조물에 인위적으로 다중 손상을 생성시켜 실험을 수행하였다. In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.