• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• 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.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.257-267
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• 2013

3-D ore modeling was performed to understand the configuration of ore bodies by integrated analysis of borehole and geophysical data in iron-mine area. Five representative indices of rocks were designated, which were obtained from geological survey and borehole. The five indices of rocks were geostatistically simulated by Sequential Indicator Simulation method to delineate boundary of the ore bodies. And Ordinary Kriging and Sequential Gaussian Simulation was applied to make secondary information using resistivity data from magnetotellurics and DC resistivity survey, and this information was used for simple kriging with local varying means, one of integrated kriging techniques. From the correlation analysis between each properties, it was found that high grade of ore is characterized by increased density, whereas the electrical resistivity decreases. With the integrated results of geophysical and borehole data, it was also found that the real configuration of ore body was similar to the modeled result and information about ore grade in 3-D space was obtained.

• Geophysics and Geophysical Exploration
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• v.15 no.4
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• pp.177-189
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• 2012

Evaluation of three-dimensional ore body modeling has been performed by applying the geostatistical integration technique to multiple geophysical (electrical resistivity, MT) and geological (borehole data, physical properties of core) information. It was available to analyze the resistivity range in borehole and other area through multiple geophysical data. A correlation between resistivity and density from physical properties test of core was also analyzed. In the case study results, the resistivity value of ore body is decreased contrast to increase of the density, which seems to be related to a reason that the ore body (magnetite) includes heavy conductive component (Fe) in itself. Based on the lab test of physical properties in iron mine region, various geophysical, geological and borehole data were used to provide ore body modeling, that is electrical resistivity, MT, physical properties data, borehole data and grade data obtained from borehole data. Of the various geostatistical techniques for the integrated data analysis, in this study, the SGS (sequential Gaussian simulation) method was applied to describe the varying non-homogeneity depending on region through the realization that maintains the mean and variance. With the geostatistical simulation results of geophysical, geological and grade data, the location of residual ore body and ore body which is previously reported was confirmed. In addition, another highly probable region of iron ore bodies was estimated deeper depth in study area through integrated modeling.