• Journal of the Korean Society for Advanced Composite Structures
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• v.1 no.2
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• pp.29-35
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• 2010

In addition to the Young's modulus, the Poisson's ratio is also at the center of attention in the field stochastic finite element analysis since the parameters play an important role in determining structural behavior. Accordingly, the sole effect of this parameter on the response variability is of importance from the perspective of estimation of uncertain response. To this end, a formulation to determine the response variability in laminate composite plates due to the spatial randomness of Poisson's ratio is suggested. The independent contributions of random Poisson's ratiocan be captured in terms of sub-matrices which include the effect of the random parameter in the same order, which can be attained by using the Taylor's series expansion about the mean of the parameter. In order to validate the adequacy of the proposed formulation, several example analyses are performed, and then the results are compared with Monte Carlo simulation (MCS). A good agreement between the suggested scheme and MCS is observed showing the adequacy of the scheme.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.396-399
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• 2011

격납건물의 내압취약도 평가를 위하여 격납건물 구조재료 변수 각각의 재료특성 시험 결과를 분석하여 현재상태 중앙값 및 변동계수 값을 추정하였다. 추정된 값은 최근의 가동중 검사 결과와 큰 차이를 보이지는 않는 것으로 나타났다. 또한 추출된 구조재료 변수들의 재조합을 통해 상관성을 배제시키는 방법을 적용함으로서, 변수 간의 상관성이 격납건물 취약도 평가에 미치는 영향을 기존의 방법과 대비하여 분석하였다. 본 예제의 경우에는 의도치 않은 변수간 상관성에 의해 2%가량 내압성능이 작게 평가되는 결과가 발생하는 것으로 나타났다. 일반적인 문제의 경우 구조재료 변수의 특성과 추출된 변수의 임의성에 의해 영향이 증폭될 수 있기 때문에, 보다 합리적인 내압취약도 평가를 위해서는 상관관계 영향을 고려하는 것이 바람직하다고 할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.96-96
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• 2018

최근 기후변동성으로 유발되는 불안정한 기상상태를 효과적으로 관측하고자 기상레이더가 도입되고 있다. 기상레이더는 경험식으로 산정된 Z-R 관계식을 통하여 레이더 강수량을 제시하게 된다. 이 과정에서 레이더 강수량은 필연적으로 실제 지상에 도달하는 강수량과는 정량적으로 오차가 발생하게 된다. 레이더 강수량에 포함된 오차는 다양한 원인으로 발생하게 되므로 레이더 강수량의 오차 성분을 규명하는 것은 레이더 강수량 활용을 위하여 필수적으로 선행되어야 한다. 본 연구는 지상강수량과 레이더 강수량의 편의를 보정하기 위한 확률통계학적 방법론을 개발하였다. 레이더 강수량의 편의오차를 보정하기 위하여 수문통계학에서 널리 활용되고 있는 계층적 Bayesian 구조를 기반으로 하였으며 자료통합(data pooling) 기법을 이용하여 편의보정 매개변수 추정과정의 불확실성 추정 효율성을 증대시켰다. 본 연구를 통하여 개발된 레이더 강수량 편의보정기법은 계층적 Bayesian 구조를 도입함으로써 편의보정 매개계수의 불확실성을 정량적으로 제시하였으며 유역 단위의 강수상관성을 현실적으로 복원하는 것을 확인하였다. 따라서 본 연구에서 제안하는 편의보정기법은 편의보정 과정에서 발생할 수 있는 매개변수의 불확실성 및 레이더 강수량의 오차구조를 정량적으로 규명하여 고해상도의 강수정보를 생산함으로써 고도화된 수문해석을 가능케 할 것으로 판단된다.

• Journal of the Korea Concrete Institute
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• v.21 no.6
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• pp.789-796
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• 2009

The randomness in the strength of a RC member is caused mainly by the variability of the mechanical properties of concrete and steel, the dimensions of concrete cross sections, and the placement of reinforcing bars and so on . Among those variations, the randomness and uncertainty of mechanical properties of concrete, such as compressive strength, tensile strength, and elastic modulus give the most significant influences and show relatively large statistical variations. In Korea, there has been little effort for the construction of its own statistical models for mechanical properties of concrete and steel, thus the foreign data have been utilized till now. In this paper, variability of compressive strength, tensile strength and elastic modulus of normal-weight structural concrete with various specified design compressive strength levels are examined based on the data obtained from a number of published and unpublished sources in this country and additional laboratory tests done by the authors. The inherent probabilistic models for compressive and tensile strength of normal-weight concrete are proposed as Gaussian distribution. Also, the relationships between compressive and splitting tensile strength and between compressive strength and elastic modulus in current KCI Code are verified and new ones are suggested based on local data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.349-358
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• 2008

In performing the reliability analysis for predicting the settlement with time of alluvial clay layer at Kobe airport, the uncertainties of geotechnical properties were examined based on the stochastic and probabilistic theory. By using Terzaghi's consolidation theory as the objective function, the failure probability was normalized based on AFOSM method. As the result of reliability analysis, the occurrence probabilities for the cases of the target settlement of ${\pm}10%,\;{\pm}25%$ of the total settlement from the deterministic analysis were 30~50%, 60%~90%, respectively. Considering that the variation coefficients of input variable are almost similar as those of past researches, the acceptable error range of the total settlement would be expected in the range of 10% of the predicted total settlement. As the result of sensitivity analysis, the factors which affect significantly on the settlement analysis were the uncertainties of the compression coefficient Cc, the pre-consolidation stress Pc, and the prediction model employed. Accordingly, it is very important for the reliable prediction with high reliability to obtain reliable soil properties such as Cc and Pc by performing laboratory tests in which the in-situ stress and strain conditions are properly simulated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1099-1108
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• 2017

Data points obtained by conducting repetitive experiments under identical environmental conditions are, theoretically, required to correspond. However, experimental data often display variations due to generated errors or noise resulting from various factors and inherent uncertainties. In this study, an algorithm aiming to determine valid bounds of input variables, representing uncertainties, was developed using probabilistic and statistical methods. Furthermore, a reliability assessment was performed to verify and validate applications of this algorithm using bolt-fastening friction coefficient data in a sample application.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.336-345
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• 2012

In crack growth life, uncertainties are caused by variance of geometry, applied loads and material properties. Therefore, the reliability estimation for these uncertainties is required to keep the robustness of calculated life. The stress intensity factors are the most important variable in crack growth life calculation, but its equation is hard to know for complex geometry, therefore they are processed by the finite element analysis which takes long time. In this paper, the response surface is considered to increase efficiency of the reliability analysis for crack growth life of a turbine wheel. The approximation model of the stress intensity factors is obtained by the regression analysis for FEA data and the response surface of crack growth life is generated for selected factors. The reliability analysis is operated by the Monte Carlo Simulation for the response surface. The results indicate that the response surface could reduce computations that need for reliability analysis for the turbine wheel, which is hard to derive stress intensity factor equation, successfully.

• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• Journal of the Korean Geotechnical Society
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• v.30 no.8
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• pp.25-37
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• 2014

In load and resistance factor design (LRFD) method, resistance factors are typically calibrated using resistance bias factors obtained from either only the data within ${\pm}2{\sigma}$ or the data except the tail values of an assumed probability distribution to increase the reliability of the database. However, the data selection approach has a shortcoming that any low-quality data inadvertently included in the database may not be removed. In this study, a data quality evaluation method, developed based on the quality of static load test results, the engineering characteristics of in-situ soil, and the dimension of aggregate piers, is proposed for use in constructing database. For the evaluation of the method, a total 65 static load test results collected from various literatures, including static load test reports, were analyzed. Depending on the quality of the database, the comparison between bias factors, coefficients of variation, and resistance factors showed that uncertainty in estimating bias factors can be reduced by using the proposed data quality evaluation method when constructing database.