• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.4
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• pp.671-679
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• 1999

건물의 진동에너지 소산능력을 향상하기 위하여 점탄성감쇠를 설치하게 되면 이른바 비비례 감쇠시스템이 되어 구조물은 복소수형태의 고유모드와 고유치를 가진다. 복소모드중첩법은 이러한 복소모드를 이용하여 중첩함으로써 비비례 감쇠시스템 구조물의 정확한 동적 거동을 얻을 수 있는 방법이다. 그러나 건물이 고층화되면 많은 자유도로 인하여 고유치해석 및 모드중첩과정에서 많은 시간과 노력이 필요하게 된다. 본 논문에서는 효율적인 구조물의 모형화를 위하여 강막가정과 행렬응축기법을 적용하였다. 또한 몇 개의 주요 모드만을 선택하여 중첩하는 방법에 대하여 연구하였으며 구조물의 진동에 영향을 주는 모드의 선택을 위한 복소모드 응답참여계수를 제안하였다. 제안된 해석방법의 정확성과 효율성을 검토하기 위하여 예제 구조물을 대상으로 해석한 결과, 응답의 정확성을 유지하면서 해석에 필요한 시간을 대폭 절감할 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.147-154
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• 2019

The finite element model updating can be defined as the problem of finding the parameters of the finite element model which gives the closest response to the actual response of the structure by measurement. In the previous researches, optimization based methods have been developed to minimize the error of the response of the actual structure and the analytical model. In this study, we propose an inverse eigenvalue problem that can directly obtain the parameters of the finite element model from the target mode information. Deep Neural Networks are constructed to solve the inverse eigenvalue problem quickly and accurately. As an application example of the developed method, the dynamic finite element model update of a suspension bridge is presented in which the deep neural network simulating the inverse eigenvalue function is utilized. The analysis results show that the proposed method can find the finite element model parameters corresponding to the target modes with very high accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.35-42
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• 2021

This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.10
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• pp.989-997
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• 2002

This paper presents a new technique that enhances the performance of the smart antenna system especially in signal environments of wide angular spread by adopting a weight vector obtained from two eigenvectors of theautocovariance matrix of the received data. While the conventional beamformingtechnique employs only one eigenvector corresponding to the largest eigenvalue, the proposed algorithm uses two eigenvectors corresponding to the largest and second largest eigenvalue in such a way that it can be robust enough to the signal environments of wide angular spread. An efficient adaptive procedure is shown to verify that the optimal weight vector consisting of the two eigenvectors is obtained with a reasonable complexity(3.5$N_2$+ 12N) and accuracy. it is also shown in this paper that the numerical results obtained from the proposed adaptive procedure well agree with those obtained from a commercial tool computing the eigen-function of MATLABTM.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.73-82
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• 2008

In this paper, a new regularization method by parameter subset selection method is proposed based on the residual force vector for damage localization. Although subset selection using the fundamental modal characteristics as a residual function has been successful in detecting a single damage location, this method seems to have limited capabilities in the detection of multiple damage locations and typically requires cumbersome weighting values. The method is presented herein and considers cases in which damage detection must be achieved using incomplete measurements of the structural responses. Model expansion is incorporated to deal with this challenge. The unique advantage of employing the new regularization method is that it can reliably identify multiple damage locations. Through an illustrative example, the proposed damage detection method is demonstrated to be a reliable tool for identifying multiple damage locations for a planar truss structure.

• Computational Structural Engineering
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• v.4 no.2
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• pp.111-117
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• 1991

The analysis method calculating the mean and standard deviation for the eigenvalue of complicated structures in which the limit state equation is implicitly expressed is formulated and applied to the buckling analysis by combining probabilistic finite element method with direct differential method which is a kind of sensitivity analysis technique. Also, the probability of buckling failure is calculated by combining classical reliability techniques such a MVFOSM and AFOSM. As random variables external load, elastic modulus, sectional moment of inertia and member length are chosen and Parkinson's iteration algorithm in AFOSM is used. The accuracy of the results by this study is verified by comparing the results with the crude Monte Carlo simulation and Importance Sampling Method. Through the case study of some structures the important aspects of buckling reliability analysis are discussed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.187-190
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• 2010

기초세굴에 따른 교각에서의 1차 모드 고유진동수 변화를 이용하여 교량 세굴 건전성을 정량적으로 평가하기 위하여 공용중인 광탄1교의 T형 교각 1기에 대하여 충격진동실험을 수행하였으며, 교축직각방향의 1차 모드 고유진동수를 수치해석결과와 비교하였다. 교량 상부구조물의 모델링 방법에 따라 상부구조물을 전체 모델링한 방법과 교각만을 모델링하고 상부구조물의 영향범위를 가정하여 교량받침 위치에 질점질량으로 변환하여 적용한 방법으로 구분하여 수치해석을 수행하였다. 상부구조물을 전체 모델링한 경우와 집중하중 형태로 상부구조물을 모델링한 경우 모두 고유진동수는 충격진동실험결과와 큰 차이를 보였다. 충격진동실험을 통해 얻은 고유진동수는 교각의 국부적인 진동수로 사료되는 반면, 수치해석을 통해 구한 결과는 교량 상부와 하부의 Coupling 효과가 반영된 시스템 전체의 고유진동수이기 때문에 결과의 큰 편차가 생기는 것으로 판단된다. 따라서 충격진동실험에서 구한 고유치 양상을 올바로 구현하기 위한 모델링을 찾기 위해서는 새로운 접근 방법이 필요할 것으로 사료된다.

• Journal of Korean Society of Steel Construction
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• v.15 no.2
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• pp.117-124
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• 2003

Wind-induced motions, like acceleration for instance, often influence designs for high-rise buildings. As a consequence, correct assessment of natural frequency becomes important. The empirical expressions used to quantify this parameter at the design phase tend to yield values that are significantly different from each other. This paper is concerned with the natural periods of steel buildings. It describes the vibration measurement methods that were employed for testing buildings. This paper will also present reliable methods of assessing the natural period from ambient vibration tests. Data from measurements on 21 buildings in Seoul were provided while 21 buildings were tested by ambient vibration measurements to obtain the natural periods. While regression formulas of natural periods for steel-frarried tall buildings were suggested,the obtained formula was compared with the empirical expressions of structural standards and the Eigen-value analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.414-416
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• 2012

고차 주성분 방법으로는 텐서 분석이 있었다. Electroencephalography(EEG) 데이터나 Social Network 데이터에 텐서 분석이 적용되어 주요한 성분들을 찾는 연구들이 있었다. 그러나 텐서 분석은 직관적으로 이해하기에 어려움이 있으며 중요한 노드를 찾는데에는 다소 어려움이 있다. 본 논문에서는 고차 하이퍼에지로 이차원 행렬을 만들고 주성분분석법을 이용하여 중요한 노드를 찾는 새로운 방법론을 제시한다. 데이터로는 Multimodal Memory Game(MMG) 수행시 촬영한 EEG 데이터를 사용하였다. MMG는 TV 드라마 기반의 기억인출게임이다. 베타파의 Power Spectrum Density(PSD)는 각 위치의 채널들의 활성도를 나타내는 지표이다. 우리는 Random Sampling을 바탕으로 PSD 상위 50%의 채널들간의 전이행렬을 구하였다. 그 후 고유치와 고유벡터를 구하였다. 가장 큰 고유치의 고유벡터는 주성분을 나타내며 고유벡터의 각 원소들은 중요도를 나타내는 centrality 이다. 세 명의 피험자에 대한 centrality 상위 30개의 중요한 채널들을 구하였고 세명에 공통적으로 포함되는 채널을 확인하였다.

• Communications of Mathematical Education
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• v.22 no.4
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• pp.445-457
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• 2008

Although both theory and experiment are very important parts in sciences, especially in mathematics, theory only seems to be very important. But the subject of numerical analysis needs both theory and practice in computer. In this paper, I provide some Matlab program codes and matrices which are used in good understanding OR methods in class.