• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.6
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• pp.265-271
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• 2015

The tsunami hazard analysis is performed for testing the application of probabilistic tsunami hazard analysis to nuclear power plant sites in the Korean Peninsula. Tsunami hazard analysis is based on the seismic hazard analysis. Probabilistic method is adopted for considering the uncertainties caused by insufficient information of tsunamigenic fault sources. Logic tree approach is used. Uljin nuclear power plant (NPP) site is selected for this study. The tsunamigenic fault sources in the western part of Japan (East Sea) are used for this study because those are well known fault sources in the East Sea and had several records of tsunami hazards. We have performed numerical simulations of tsunami propagation for those fault sources in the previous study. Therefore we use the wave parameters obtained from the previous study. We follow the method of probabilistic tsunami hazard analysis (PTHA) suggested by the atomic energy society of Japan (AESJ). Annual exceedance probabilities for wave height level are calculated for the site by using the information about the recurrence interval, the magnitude range, the wave parameters, the truncation of lognormal distribution of wave height, and the deviation based on the difference between simulation and record. Effects of each parameters on tsunami hazard are tested by the sensitivity analysis, which shows that the recurrence interval and the deviation dominantly affects the annual exceedance probability and the wave heigh level, respectively.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.46-48
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• 2005

This paper discusses a probabilistic method for power system security assessment. The security analysis relates to the ability of the electric power systems to survive sudden disturbances such as electric short circuits or unanticipated loss of system elements. It consists of both steady state and dynamic security analyses, which are not two separate issues but should be considered together. In steady state security analysis including voltage security analysis, the analysis checks that the system is operated within security limits by OPF (optimal power flow) after the transition to a new operating point. Until now, many utilities have difficulty in including dynamic aspects due to computational capabilities. On the other hand. dynamic security analysis is required to ensure that the transition may lead to an acceptable operating condition. Transient stability, which is the ability of power systems to maintain synchronism when subjected to a large disturbance. is a principal component in dynamic security analysis. Usually any loss of synchronism may cause additional outages and make the present steady state analysis of the post-contingency condition inadequate for unstable cases. This is the reason for the need of dynamic studies in power systems. Probabilistic criterion can be used to recognize the probabilistic nature of system components while considering system security. In this approach. we do not have to assign any predetermined margin of safety. A comprehensive conceptual framework for probabilistic static and dynamic assessment is presented in this paper. The simulation results of the Western System Coordinating Council (WSCC) system compare an analytical method with Monte-Carlo simulation (MCS).

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.111-123
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• 2008

Geotechnical engineering problems are characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for a probabilistic analysis that considers the spatial variability of soil properties is presented to study the response of spatially random soil. The approach integrates a commercial finite difference method and random field theory into the framework of a probabilistic analysis. Two-dimensional non-Gaussian random fields are generated based on a Karhunen-$Lo{\grave{e}}ve$ expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to study the effects of uncertainty due to the spatial heterogeneity on the settlement and bearing capacity of a rough strip footing. The simulations provide insight into the application of uncertainty treatment to the geotechnical problem and show the importance of the spatial variability of soil properties with regard to the outcome of a probabilistic assessment.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.51-55
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• 2014

Based on the results of Probabilistic Safety Assessment(PSA) for a Nuclear Power Plant (NPP), Common Cause Failure(CCF) events have been recognized as one of the main contributors to the risk. Also, the CCF data and estimation method used in domestic PSA models have been pointed out as an issue with respect to the quality. The existing method of MGL and non-staggered testing even widely used were considered conservative in estimating the safety and had a limited capability in uncertainty analyses. Therefore, this paper presents the CCF estimation using a new generic data source and Alpha factor method. The analyses showed that Alpha factor and staggered method are effective in estimating the CCF contribution and risk insights of reference plant. This method will be a common bases for the optimization of new design for the construction plants as well as for the updating of safety assessment on the operating nuclear power plants.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1340-1343
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• 1999

This Paper Presents a new analytic load decrement method for the evaluation of avoided generation costs of independent power producers (IPPs), named as probabilistic load decrement method. Unlike conventional load decrement methods, the proposed method exactly consider the random outage characteristic of a generating unit, economic dispatch order, and the resulting loss of load probability. Therefore, we can Provide the exact generation avoided costs of an IPP by applying the developed method. In the case studies, we have shown the correctness and effectiveness of the method, and compared with conventional load decrement methods.

• Nuclear Engineering and Technology
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• v.28 no.5
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• pp.440-451
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• 1996

With emphasis on safety, this study addresses for better design condition for the cooling system in a wet-type interim spent fuel storage facility, using a probabilistic safety assessment method. To incorporate the design renovation into the design phase, a simple approach is proposed. By taking the cooling system of a reference design, a fault tree analysis was performed to identify the weak point of the considered system, and then basic factors for design renovation were defined. A total of 21 design alternatives were selected through the combination of the basic factors. Finally, the optimum design alternative for the cooling system is derived by means of the cost and effect analysis based on the estimated cost, system reliability and assumed probabilistic safety criteria. With the assumption that the failure frequency of at-reactor spent fuel cooling system compiles with probabilistic safety criteria for the interim spent fuel cooling system, it was shown that the optimum alternative should have l00% cooling loop redundancy with one pump per cooling loop and a cleanup system installed separately from the main loop. Furthermore, it also should be classified into safety system. The result of this study can be used as a useful basis to identify factors of safety concern and to establish design requirements in the future. The method also can be applied for other nuclear facilities.

• Smart Structures and Systems
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• v.21 no.5
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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.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.545-548
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• 2008

As the corrosion of reinforcing bar in concrete structures exposed to chloride attack is one of main factors to determine the remaining service life, marine concrete structures have to be designed to protect the chloride penetration. Among the durability design methods such as deterministic method and probabilistic method, design method based on the probabilistic theory has been widely studied. However, the most essential material, data of the material properties related to chloride diffusion, are still insufficient. In this paper, the probabilistic distribution of chloride diffusion coefficients and aging coefficients are derived by the experiment and analysis for the chloride coefficients of concrete containing pozzolans, which are generally used in marine structures.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.191-192
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• 2017

In response to the economic depression, the demand for fixed rent income has increased according to the easing construction regulations. it caused indiscriminated investment to stakeholders. This leads to oversupply in the multi-family Housing market and increases unsold housing and vacancy rates except specific area such as Gangnam-gu.In order to solve this issue, although studies on the optimization price of apartment houses has been conducted, the study is insufficient regarding on residential officetel. Therefore, the objective is to suggest a basic study on optimal price estimation model development by using probabilistic forecasting method in planning phase. To achieve the objective, first, variables are defined such as expenses, financial costs, income, etc. Second, causal loop diagram is suggested. Third, basic optimization prices estimation model is developed. In the future, this study can be used as one of decision making tools in planning phase of officetel development projects.