• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.65-70
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• 2008

The capacity spectrum method (CSM) is a deterministic seismic analysis approach wherein the expected seismic response of a structure is established as the intersection of the demand and capacity curves. Recently, there are a few studies about a probabilistic CSM where uncertainties in design factors such as material properties, loads, and ground motion are being considered. However, researches show that soil-structure interaction also affects the seismic responses of structures. Thus, their uncertainties should also be taken into account. Therefore, this paper presents a probabilistic approach of using the CSM for seismic analysis considering uncertainties in soil properties. For application, a reinforced concrete bridge column structure is employed as a test model. Considering the randomness of the various design parameters, the structure's probability of failure is obtained. Monte Carlo importance sampling is used as the tool to assess the structure's reliability when subjected to earthquakes. In this study, probabilistic CSM with and without consideration of soil uncertainties are compared and analyzed. Results show that the analysis considering soil structure interaction yields to a greater probability of failure, and thus can lead to a more conservative structural design.

• Geomechanics and Engineering
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• 제7권5호
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• pp.525-537
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• 2014

The typical design of ground improvement with prefabricated vertical drains (PVD) and surcharge preloading involves a series of deterministic analyses using averaged or mean soil properties for the various combination of the PVD spacing and surcharge preloading height that would meet the criteria for minimum consolidation time and required degree of consolidation. The optimum design combination is then selected in which the total cost of ground improvement is a minimum. Considering the variability and uncertainties of the soil consolidation parameters, as well as considering the effects of soil disturbance (smear zone) and drain resistance in the analysis, this study presents a stochastic cost optimization of ground improvement with PVD and surcharge preloading. Direct Monte Carlo (MC) simulation and importance sampling (IS) technique is used in the stochastic analysis by limiting the sampled random soil parameters within the range from a minimum to maximum value while considering their statistical distribution. The method has been verified in a case study of PVD improved ground with preloading, in which average results of the stochastic analysis showed a good agreement with field monitoring data.

• 물과 미래
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• 제26권2호
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• pp.79-87
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• 1993

크리깅을 이용하여 평균면적우량을 추정함에 있어 새로운 방법을 개발하였다. 이 방법은 크리깅 방정식에 나타나는 2중 및 4중 수치적분에 필요한 점들을 대상지역의 경계만 주면 임의로 추출하여 사용한다는 것이 기존의 방법과는 상이하다. 이로 인해 대상지역을 소구역으로 나누고, 각각의 중심점을 계산해야 했던 기존 방식의 난점을 극복하였으며, 따라서 본 연구에서 개발된 방법은 복잡한 경계를 갖는 지역의 경우 더욱 유용하다. 경계가 주어지면 그 안에서 점들을 임의로 추출하는 과정을 복소함수론에 기초한 알고리듬을 통하여 설명하였다. Monte Carlo 시뮬레이션의 결과, 개발된 방법에 의한 평균면적유량의 추정에 따른 오차는 추출점 수의 제곱근에 반비례하였다.

• 조명전기설비학회논문지
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• 제21권10호
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• pp.52-58
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• 2007

본 논문에서는 발전량 적정성 평가를 위한 풍력발전 모형의 제안하였다. 풍력 발전량과 계통 부하량은 일 년을 주기로 하는 주기함수 형태이므로, 둘 중 하나의 물리량이 주어지면 다른 물리량의 발생 확률을 계산할 수 있다. 본 논문에서는 가상의 데이터를 바탕으로 두 물리량을 k-means 클러스터링 알고리즘으로 단계화하였고, 각 단계간의 확률적인 관계를 계산하였다. 제안하는 풍력발전 모형은 상태샘플링(state sampling)에 기반을 둔 몬테카를로 모의로써 발전량 적정성을 평가하는데 적합하다.

• 대한토목학회논문집
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• 제14권5호
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• pp.1043-1053
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• 1994

본 논문에서는 태풍이나 착빙설 등 기상관련 하중을 받는 송전철탑의 실용적이고 합리적인 설계를 위하여 신뢰성에 기초한 하중저항계수설계기준(Load and Resistance Factor Design : LRFD)을 개발하였다. 이때, 설계풍하중 및 착빙하중은 송전철탑에 가해지는 풍속과 착빙설에 대한 우리나라의 가용한 통계자료를 바탕으로 MCS(Monte Carlo Simulation) 기법을 사용하여 추정하였다. 시설 송전철탑의 요소 및 체계신뢰성해석에는 AFOSM(Advanced First Order Second Moment)신뢰성방법과 IST(Importance Sampling Technique)를 사용하였다. LRFD 설계기준의 하중 및 저항계수는 합리적으로 선정된 목표 신뢰도를 기초로 AFOSM과 code 최적화기법을 사용하여 도출하였다.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 대한기계학회논문집A
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• 제26권4호
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• pp.653-659
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• 2002

This paper describes a probabilistic fracture mechanics(PFM) analysis based on Monte Carlo(MC) simulation. In the analysis of CANDU pressure tube, the depth and aspect ratio of an initial semi-elliptical surface crack, a fracture toughness value and delayed hydride cracking(DHC) velocity are assumed to be probabilistic variables. As an example, some failure probabilities of piping and CANDU pressure tube are calculated using MC method with the stratified sampling MC technique, taking analysis conditions of normal operations. In the stratified MC simulation, a sampling space of probabilistic variables is divided into a number of small cells. For the verification of analysis results, a comparison study of the PFM analysis using other commercial code is carried out and a good agreement was observed between those results.

• 방사성폐기물학회지
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• 제19권4호
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• pp.447-457
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• 2021

Graphite Isotope Ratio Method (GIRM) can be used to estimate plutonium production in a graphite-moderated reactor. This study presents verification results for the GIRM combined with a 3-D polynomial regression function to estimate cumulative plutonium production in a graphite-moderated reactor. Using the 3-D Monte-Carlo method, verification was done by comparing the cumulative plutonium production with the GIRM. The GIRM can estimate plutonium production for specific sampling points using a function that is based on an isotope ratio of impurity elements. In this study, the ¹⁰B/¹¹B isotope ratio was chosen and calculated for sampling points. Then, 3-D polynomial regression was used to derive a function that represents a whole core cumulative plutonium production map. To verify the accuracy of the GIRM with polynomial regression, the reference value of plutonium production was calculated using a Monte-Carlo code, MCS, up to 4250 days of depletion. Moreover, the amount of plutonium produced in certain axial layers and fuel pins at 1250, 2250, and 3250 days of depletion was obtained and used for additional verification. As a result, the difference in the total cumulative plutonium production based on the MCS and GIRM results was found below 3.1% with regard to the root mean square (RMS) error.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3073-3084
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• 2022

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.

• Wind and Structures
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• 제33권6호
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.