• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.333-336
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• 2004

원자력 구조물, 항공기 구조물 등의 손상은 각 구조물의 손상에 의해서 발생하는 충격파의 탑지로서 손상의 위치를 탐지 할 수가 있다. 이러한 손상의 위치를 탐지하기 위한 역변환 문제는 오랜 기간 동안 중요한 연구의 과제가 되고 있다. 본 연구에서는 신경망 회로 기술을 이용하여 이러한 충격파를 탐지하고자 하며, 이 기술의 검증을 위해서 평판에서 실험을 실행하여 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.31-38
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• 2003

In the present work, a nonlinear inverse perturbation method which has been used in the structural optimization, is adopted so as to identify the structural damages. Unlike the structural optimization, a larger number of constrained equations than the number of unknown parameters are often required detect structural damage. Therefore, nonlinear least squares method is utilized to solve the problem. Because only a limited number of sensors are available I real situation of damage detection, the determination of sensor location becomes one of the most important issues. Hence, this work concentrates on the issue of sensor placement in the framework of nonlinear inverse perturbation method, and the performances of various methodologies concerning to sensor placement are compared with each other. The comparisons show tat the successive elimination method gets good performance for sensor placement. From the several numerical studies, it is confirmed that the inverse perturbation method, combined with the successive elimination method, is very promising in structural damage detection.

• Journal of Korean Society of Steel Construction
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• v.24 no.5
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• pp.549-558
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• 2012

This study identifies the damage detection of truss structures by using genetic algorithm(GA) from changed elements properties. To model the damaged truss structures, the modulus of elasticity of some specific elements is reduced. The analysis of truss structures is performed with static analysis by applying uniform load, and the location and extent of structural damage is detected by comparing the stain of each element of healthy truss structures with damaged truss structures using genetic algorithm. In this study, some numerical examples are presented to detect the location and extent of damage using genetic algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.693-700
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• 2006

To detect and evaluate the damage present in bridge, static identification method is known to be simple and effective, compared to dynamic method. In this study, the damage detection method in steel box girder bridge using static responses including displacement, slope and curvature is examined. The static displacement is calculated using finite element analysis and the slope and curvature are determined from the displacement using central difference method. The location of damage is detected using the absolute differences of these responses in intact and damaged bridge. Steel box girder bridge with corner crack is modeled using singular element in finite element method. The results show that these responses were significantly useful in detecting and predicting the location of damage present in bridge.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.623-626
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• 2011

본 연구는 수중 강판에 존재하는 결함탐지를 위한 탄성파 유한요소 시뮬레이션이다. 일반적으로 수중 강판은 외부의 물로 인하여 결함의 탐지가 어렵다. 이러한 수중 강판의 결함탐지에는 잠수부가 수중 강판 표면에 비파괴 검사 장비를 활용하여 결함을 탐지하는 경우가 많으며 잠수부의 경험과 많은 시간이 소요되는 단점이 있다. 본 연구에서는 수중강판의 표면이 아닌 수중에서 탄성파를 발생시켰을 경우 수중 강판의 결함탐지 유한요소 시뮬레이션을 이용하여 손상의 위치와 손상의 크기에 따라 발생하는 응답을 알아보았다. 강판의 상하부에 기계적인 손상이 발생한 경우를 손상 시나리오로 가정하고 해석을 수행하였다. 손상이 없는 경우의 응답을 기준으로 강판의 상부와 하부에 기계적인 손상이 있는 경우에 발생하는 응답을 비교하였다. 동적유한요소 프로그램인 ANSYS/LS-DYNA를 사용하여 결함탐지 해석을 수행하였다. 결과적으로 손상의 종류에 따라 응답신호의 진폭 감소가 나타났으며 손상의 크기가 커질수록 진폭 감소가 커지는 결과를 나타내었다.

• 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.49 no.1
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• pp.1-12
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• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.687-691
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• 2010

구조물의 건전도를 평가하기 위해 상시 구조물 계측을 이용한 Structural Health Monitoring (SHM) 시스템을 적용하게 된다. SHM 시스템의 궁극적 목적은 계측된 데이터를 이용하여 구조물의 손상위치 및 손상정도를 분석하여 거주자에게 유지관리정보와 대처요령 신속하게 제공하는 것이다. 따라서 본 연구에서는 구조물의 손상탐지를 위해 인공신경망(Artificial Neural Network)을 도입한 알고리즘을 수립하고, 이를 3층 실대 RC Mock-up 구조물에 적용하여 성능을 평가하였다. 먼저 인공신경망의 학습을 위해 구조해석 프로그램을 이용하여 구조물의 손상에 따른 동적특성 변화 데이터베이스를 구축하였다. 그리고 학습된 인공망에 실제 구조물에서 추출한 동특성의 변화를 입력하여 손상탐지를 실시하였다. 이를 통해 인공신경망의 학습방법, 학습데이터의 정규화 방법 등을 규명하고 인공신경망을 이용한 손상탐지의 효과를 분석하였다.

• 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.