• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.125-127
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• 2011

구조물의 동적특성인 모드형상을 통해 국부적인 손상을 탐지하는 손상평가 기법은 많은 연구자에 의해 발전되고 있다. 하지만 플로팅 구조물에 대한 손상탐지 기법은 그 사례를 찾아보기 힘들다. 본 논문에서는 플로팅 구조물의 축소모형을 제작하고, 손상을 모사하여 손상전과 손상 후 구조물의 모드형상에서 얻을 수 있는 모달 변위로 나타낸 손상지수를 통해 플로팅 구조물의 손상을 탐지 하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.279-281
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• 2012

구조물의 동적특성인 모드형상을 통해 국부적인 손상을 탐지하는 손상평가 기법은 많은 연구자에 의해 발전되고 있다. 하지만 플로팅 구조물에 대한 손상탐지 기법은 그 사례를 찾아보기 힘들다. 본 논문에서는 콘크리트재질의 플로팅 구조물의 축소모형을 제작하고 손상을 모사하여 손상 전, 후 구조물의 모드형상에서 얻을 수 있는 모달 변위로 나타낸 손상지수를 통해 플로팅 구조물의 손상을 탐지하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.189-191
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• 2011

구조물의 동적특성인 모드형상을 통해 국부적인 손상을 탐지하는 손상평가 기법은 많은 연구자에 의해 발전되고 있다. 하지만 플로팅 구조물에 대한 손상탐지 기법은 그 사례를 찾아보기 힘들다. 본 논문에서는 플로팅 구조물의 함체 부분을 모델링 하고, 손상 전과 손상 후 구조물의 모드형상에서 얻을 수 있는 모달 변위로 나타낸 손상지수를 통해 플로팅 구조물에 발생할 수 있는 손상을 추정하여, 제안된 이론의 적용성을 검증 하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.5
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• pp.369-376
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• 2014

This paper presents a method that integrates passive and active-sensing techniques for the structural health monitoring of plate-like structures. Three piezoelectric transducers are deployed in a L-shape to detect and locate an impact event by measuring and processing the acoustic emission data. The same sensor arrays are used to estimate the subsequent structural damage using guided waves. Because this method does not require a prior knowledge of the structural parameters, such as the wave velocity profile in various directions, accurate results could be achieved even on anisotropic or curved plates. A series of experiments was performed on plates, including a spar-wing structure, to demonstrate the capability of the proposed method. The performance was also compared to that of traditional approaches and the superior capability of the proposed method was experimentally demonstrated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.213-216
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• 2009

유한요소모델개선기법은 계측된 동특성을 모사하는 구조해석모델을 구하는 방법으로서 손상탐지 및 구조건전도감시를 위해 효과적으로 이용될 수 있다. 유한요소모델개선기법에는 다양한 종류의 동특성데이터가 사용될 수 있는데, 본 연구에서는 고유진동수와 모드형상을 사용한 경우와 고유진동수와 주파수응답함수를 사용한 경우를 각각 사용해 실험실 규모의 구조물의 손상 위치 및 손상정도를 추정하였다. 4층 철골조의 골조구조물로서 진동대를 이용하여 원구조물에 백색잡음 가진실험을 실시한 후 손상의 모사를 위해 1층 부분의 보 부재를 작은 단면의 부재로 교체하고 동일한 실험을 반복하였다. 보 부재 교체 전 후에 계측된 데이터와 두 종류의 모델개선기법을 각각 적용하여 손상탐지를 실시한 후 그 정확도를 분석하였다.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.527-533
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• 2013

This paper presents damage detection and estimation of stiffness parameter on a concrete scale model and a real structure of concrete pontoon using dynamic properties such as mode shapes and natural frequencies. In case of damage detection, dynamic impact test on a concrete scale model is accomplished to extract mode shapes and the practicality is verified by utilizing a damage detection technique. And the stiffness parameter of a real structure of concrete pontoon was estimated via system identification technique using the natural frequencies of the structure. The results indicate that the damaged elements of the scale model are found exactly using damage detection technique and the effective stiffness property of the real structure of concrete pontoon can be estimated by system identification technique.

• Journal of Navigation and Port Research
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• v.39 no.3
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• pp.179-184
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• 2015

The purpose of this study is to provide appropriate methodology to ensure the safety and integrity of the production riser in offshore structure. In order to select integrity estimation methodology for production riser, level I and II Non-destructive Damage Evaluation (NDE) methods that were applied to existing structures are classified and reviewed. Numerical analysis is performed to verify the applicability and capability on damage detection of reviewed methods. As a result, the damage detection methodology using modal strain energy is more sensitive in detection of the damage than other methods. In practice, the number of sensors is limited due to the environmental and financial conditions. The impact on damage detection performance by reducing the number of sensors is systematically investigated through a series of numerical analyses and the results are discussed. The optimal number of sensor for the integrity estimation of production riser is recommended.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.676-679
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• 2011

인공신경망(Artificial Neural Network)을 이용하여 RC Mock-up 구조물의 손상위치 및 손상정도를 단계적으로 추정하였다. 대상 구조물은 가진실험을 통하여 구조물의 응답을 취득하고 구조물식별기법(Structural System Identification)을 통하여 구조물의 동특성을 찾았다. 유한요소해석프로그램을 사용하여 동특성이 계측치와 가장 유사한 기본해석모델을 만든 후 이 기본해석모델을 이용하여 학습데이터를 생성하였다. 기존 인공신경망을 이용한 손상탐지를 개선하고자 본 연구에서는 인공신경망 학습데이터를 분석하였고 효과적인 손상탐지를 위하여 학습데이터를 가공하였다. 가공된 학습데이터를 사용하여 단계별 손상탐지를 실시하였고 기존 손상탐지 방법보다 좋은 결과를 유도하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.98-104
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• 2002

System condensation technique improves the efficiency of the inverse perturbation method of damage detection developed in the previous work. The technique is applied to transform the unmeasured DOFs to the measured DOFs. This approach makes it possible to eliminate the unmeasured DOFs, which accelerates the computational efficiency. The numerical instability problems due to the system condensation technique are also resolved by updating the transformation matrix for each step, and also by adopting the accelerated improved reduced system(AIRS) condensation method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.