• Geomechanics and Engineering
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• v.12 no.3
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• pp.483-503
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• 2017

Uncertainty is a fact belonging to engineering practice. An important uncertainty that sets geotechnical engineering is the variability associated with the properties of soils or, more precisely, the characterization of soil profiles. The reason is due largely to the complex and varied natural processes associated with the formation of soil. Spatial variability analysis for the study of the stability of natural slopes, complementing conventional analyses, is able to incorporate these uncertainties. In this paper the characterization is performed in back-analysis for a case of landslide occurred to verify afterwards the presence of the conditions of shear strength at failure. This approach may support designers to make more accurate estimates regarding slope failure responding, more consciously, to the legislation dispositions about slope stability evaluation and future design. By applying different kriging techniques used for spatial analysis it has been possible to perform a 3D-slope reconstruction. The predictive analysis and the areal mapping of the soil mechanical characteristics would support the definition of priority interventions in the zones characterized by more critical values as well as slope potential instability. This tool of analysis aims to support decision-making by directing project planning through the efficient allocation of available resources.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.429-435
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• 2005

3D RMR (Rock Mass Rating) analysis has been performed by applying the Geostatistical integration technique for geophysical and borehole data. Of the various geostatistical techniques for the integrated data analysis, in this study, we applied the SKlvm (Simple Kriging with local varying means) method that substitutes the values of the interpreted geophysical result with the mean values of the RMR at the location to be inferred. The substitution is performed by the indicator transform between the result of geophysical interpretation and the observed RMR values at borehole sites. The used geophysical data are the electrical resistivity and MT result, and 10 borehole sites are investigated to obtain the RMR values. This integrated analysis makes the interpretation to be more practical for identifying the realistic RMR distribution that supports the regional geological situation.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.241-250
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• 2020

In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.40-48
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• 2005

This paper will specify geo-objects and geo-fields of the geo-ecological contamination source and implement the system for evaluating an ocean Environmental contamination based on the spatial statistical analysis. In order to produce the grade of fishery that can evaluate the ocean effect, we will analysis the degree of the spatial correlation by semi-veriogram and predicate the elevation raster of spatial data using ordinary kriging method. This paper is to estimate the grade of fishery contamination region and produce the ratio of the area according to the fishery grade. Therefore, we can contribute to produce fishery grade that evaluates the ocean effect by means of deciding an efficient fishery environment.

• Spatial Information Research
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• v.10 no.2
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• pp.233-246
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• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.343-350
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• 2022

High-resolution data are needed to understand water body mixing patterns at river junctions. In particular, in river analysis, hydrological and water quality characteristics are used as basic data for aquatic ecological health, so observation through continuous monitoring is necessary. In addition, since measurement is carried out through a one-dimensional and fixed measurement method in existing monitoring systems, a hydrological and water quality characteristics investigation of an entire river, except for in the immediate vicinity of the measurement point, is not undertaken. In order to obtain high-resolution measurement data, a measurer has to consider multiple factors, and the area or time that can be measured is limited. Although the resolution might be lowered, an appropriate interpolation method must be selected in order to acquire a wide range of data. Therefore, in this study, a high-elevation measurement method at a river junction was introduced, and the interpolation method according to the measurement results was compared. The overall hydraulic and water quality information of the river was indicated through the visualization of the prediction and interpolation method in the low-resolution measurement result. By comparing each interpolation method, Inverse Distance Weighting, Natural Neighbor, and Kriging techniques were applied in river mapping to improve the precision of river mapping through visualized data and quantitative evaluation. It is thought that this study will offer a new method for measuring rivers through spatial interpolation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.611-617
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• 2010

A complex system involves a large number of design variables, and its operation is non-linear. To explore the characteristics in its design space, a Kriging meta-model can be utilized; this model has replaced expensive computational analysis that was performed in traditional parametric design optimization. In this study, a Kriging meta-model with a computational orthogonal array for the design of experiments was developed to optimize the fatigue life of a turbine blade whose behavior under cyclic rotational loads is significantly non-linear. The results not only show that the maximum fatigue life is improved but also indicate that the accuracy of computational analysis is achieved. In addition, the robustness of the results obtained by six-sigma optimization can be verified by comparison with the results obtained by performing Monte Carlo simulations.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.217-228
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• 2018

Since the real estate reporting system was first introduced, about 2 million real estate transaction per year have been reported over the last 10 years with an increasing demand for real estate price estimates. This study looks at the applicability and superiority of the regression-kriging method to derive effective real transaction prices estimation on the location where information about real transaction is unavailable. Several issues on predicting the real estate price are discussed and illustrated using the real transaction reports of Jinju, Gyeongsangnam-do. Results have been compared with a simple regression model in terms of the mean absolute error and root square error. It turns out that the regression-kriging model provides a more effective estimation of land price compared to the simple regression model. The regression-kriging method adequately reflects the spatial structure of the term that is not explained by other characteristic variables.