• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1991년도 가을 학술발표회 논문집
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• pp.57-62
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• 1991

For the purpose of developing the method for efficiently calculating the design sensitivity and the reliability for the complicated structure such as ship structure, the probabilistic finite element method is introduced to formulate the deterministic design sensitivity analysis method and incorporated with the second moment reliability methods such as MVFOSM, AFOSM and SORM. Also, the probabilistic design sensitivity analysis needed in the reliability-based design is performed. The reliability analysis is carried out for the initial yielding failure, in which the derivative derived in the deterministic desin sensitivity is used. The present PFEM-based reliability method shows good agreement with Monte Carlo method in terms with the variance of response and the associated probability of failure even at the first or first few iteration steps. The probabilistic design sensitivity analysis evaluates explicitly the contribution of each random variable to probability of failure. Further, the reliability index variation can be easily predicted by the variation of the mean and the variance of the random variables.

• 자원환경지질
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• 제33권4호
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• pp.295-307
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• 2000

확률론적 해석방법 (probabilistic analysis)은 현장으로부터 획득한 자료들에서 일반적으로 발생하는 가변성과 불확실성을 효과적으로 정량화하여 해석에 이용할 수 있는 방법 중의 하나로 제안되었다. 특히 암반사면공학에서는 이러한 가변성과 불확실성이 불연속면의 방향 및 기하학적 특성, 그리고 실내실험 결과의 분산으로 나타난다. 확률론적 해석방법은 불연속면의 기하학적 특성과 강도 특성을 확률변수 (random variable)로 취급하여 신뢰성이론 (reliability theory)과 확률이론 (probability theory)을 근거로 분석하였으며 이를 기초로 하여 Monte Carlo Simulation과 같은 해석법을 이용, 구조물의 붕괴가능성을 확률로 표현하였다. 확률론적 해석 방법은 기존의 안전율을 대체하여 구조물의 안정성을 붕괴확률 (probability of failure)로 제안하였으며 이 붕괴확률은 안전율의 확률분포함수 (probability density function)에서 안전율이 1보다 작을 가능성을 확률로 나타낸 수치이다. 이 방법은 안전율의 개념을 기초로 하여 자료의 분산을 고려하지 않은 채 단일 대표 값만을 이용하여 구조물의 안정성을 판단하는 전통적인 결정론적 해석방법 (deterministic analysis)과 비교되어진다. 본 논문에서는 확률론적 해석방법을 이용하여 불연속면 특성들의 확률특성을 고찰하였으며 이를 기초로 하여 암반사면의 안정성 해석에 응용했다. 또한 확률론적 해석과 결정론적인 해석의 결과를 비교, 그 차이점을 설명하고자 하였다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 지반공학 공동 학술발표회
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• pp.344-351
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• 2005

In this paper, a reliability-groundwater flow program is developed by coupling the 2-D finite element numerical groundwater flow program with first and second order reliability program. From the parametric study of hydraulic conductivity of soil layers, the increase of both mean and variance of hydraulic conductivity results in the increase of probability of exceeding the threshold hydraulic head. The probability of failure was more sensitive to parameters of weathered granitic soil and rock located at the middle and bottom of the excavation than those at the other locations. It can be recommended from this study that the reliability method, which can include the uncertainty of soil parameters, should be performed together with the deterministic analysis to compensate the weakness of the latter analysis for the groundwater flow problem of underground excavations.

• 한국전산유체공학회지
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• 제11권1호
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• pp.21-28
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• 2006

Reliability Based Design Optimization(RBDO) is one of the optimization methods that minimize the product failure due to small changes of operating conditions or process errors. It searches the optimum that satisfies the safety margin of each constraint, and it gives stable and reliable designs. However, RBDO requires many times oj computational efforts compared with the conventional deterministic optimization(DO) to evaluate the probability of failure about each constraint, therefore it is hard to apply directly to large-scaled problems such as a flexible wing shape design optimization. For the efficient reliability analysis, the approximate reliability analysis method with the two-point approximation(TPA) is proposed In this study, the lift-to-drag ratio maximization designs are performed with 3-dimensional Navier-Stokes analysis and NASTRAN structural analysis, and the optimization results about the deterministic, FORM and SORM are compared.

• Structural Engineering and Mechanics
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• 제26권3호
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• 한국지반공학회논문집
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• 제18권4호
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• pp.119-126
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• 2002

본 논문에서는 암반사면의 절토시 현장지반 시추조사의 불충분한 자료와 비균질한 지반특성에서 오는 불확실성이 암사면의 파괴확률에 미치는 영향을 알아보기 위하여 신뢰성해석을 실시하였고 이를 확정론적 해석결과와 비교하였다. 해석에 사용된 확률변수는 절리면의 점착력과 마찰각, 암반의 단위중량 이었으며 절리면의 경사각은 확률변수로 적용한 경우와 그렇지 않은 경우로 나누어 해석하였다. 해석 결과 대상 절토사면의 경우 확정론적 해석시 안정하게 나타남에도 신뢰도 해석에서는 연약 지질층의 영향에 의한 높은 불명확성으로 인하여 높은 파괴확률을 갖는 것으로 나타났다. 각 확률변수의 평균값과 분산에 대한 파괴확률의 민감도는 점착력이 가장 큰 것으로 나타났으며 평균값이 미치는 민감도가 분산의 경우보다 크게 나타나 현장 지반물성치들에 대한 평균값의 정확한 산출이 중요한 것으로 나타났다.

• 대한기계학회논문집A
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• 제27권7호
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• pp.1234-1242
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• 2003

In this paper we present a simple and efficient robust optimal design formulation for MEMS structures and its application to a resonant-type micro probe. The basic idea is to use the gradient index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-effective and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation. This method, although shown for MEMS structures, may as well be easily applied to conventional mechanical structures where information on uncertainties is lacking but robustness is highly important.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.1254-1261
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• 2003

In this paper we present a simple and efficient robust optimal design formulation and its application to a resonant-type micro probe. The basic idea is to use the Gradient Index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-efficient and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2005년도 추계학술발표논문집
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• pp.45-53
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• 2005

A novel method for evaluating the anisotropy of the deterministic features in a stochastic 2D data is introduced. The ability of the wavelet transform for the identification of the abrupt discontinuities could be used to characterize the boundary of the deterministic area in a 2D stochastic data, such as flocs in paper structure. The one-dimensional wavelet transform with a small-scale range in MD and CD could quantify the amount of the edge in both directions, depending on the intensity of each floc. The flocs that are aligned in the MD direction result in a higher value of local wavelet energy in the CD direction. Therefore, the ratio of the total wavelet energy in CD and MD directions can be used as a new anisotropy index. This index is a measure of the floc-orientation and can provide an excellent tool to obtain the orientation distribution and the major oriented angle of flocs. Various simulated images and real stochastic data such as local gloss variation of printed image and formation image, have been tested and the results show this analysis method is very reliable to measure the anisotropy of the deterministic features.

• 대기
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• 제30권4호
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.