• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1435-1440
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• 2009

Geotechnical variability is a complex feature that results from many independent sources of uncertainties, and is mainly affected by inherent variability and measurement errors. This study evaluates the coefficient of variation (COV) of soil properties at Song-do region in Korea for evaluating inherent soil variability. Since soil variability is sensitive to soil layers and soil types, the COVs by soil layers (reclaimed layer and marine layer) and the COVs by soil types (clay and silt) were separately evaluated. It is observed that geotechnical variability of marine layer and clay is relatively smaller than that of reclamation layer and silt.

• Journal of the Korean Geotechnical Society
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• v.25 no.6
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• pp.73-88
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• 2009

Geotechnical variability is a complex feature that results from many independent sources of uncertainties, and is mainly affected by inherent variability and measurement errors. This study evaluates the coefficient of variation (COV) of soil properties and soil layers at Song-do region in Korea. Since soil variability is sensitive to soil layers and soil types, the Cays by soil layers (reclaimed layer and marine layer) and the COVs by soil types (clay and silt) were separately evaluated. It is observed that geotechnical variability of marine layer and clay is relatively smaller than that of reclamation layer and silt. And, the highly weathered rock and soil show the higher cays in the interpretation of the strength parameters of the fresh and weathered rock. And the proposed COV of Songdo area can be used for the reliability-based design procedure.

• Geomechanics and Engineering
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• v.20 no.1
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• pp.19-28
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• 2020

The spatial variability of geotechnical properties can lead to the uncertainty of settlement for frozen soil foundation around the oil pipeline, and it can affect the stability of permafrost foundation. In this paper, the elastic modulus, cohesion, angle of internal friction and poisson ratio are taken as four independent random fields. A stochastic analysis model for the uncertain settlement characteristic of frozen soil foundation around an oil pipeline is presented. The accuracy of the stochastic analysis model is verified by measured data. Considering the different combinations for the coefficient of variation and scale of fluctuation, the influences of spatial variability of geotechnical properties on uncertain settlement are estimated. The results show that the stochastic effects between elastic modulus, cohesion, angle of internal friction and poisson ratio are obviously different. The deformation parameters have a greater influence on stochastic settlement than the strength parameters. The overall variability of settlement reduces with the increase of horizontal scale of fluctuation and vertical scale of fluctuation. These results can improve our understanding of the influences of spatial variability of geotechnical properties on uncertain settlement and provide a theoretical basis for the reliability analysis of pipeline engineering in permafrost regions.

• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.87-103
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• 2009

To determine the reliable probabilistic distribution model of geotechnical properties, outlier and randomness test for analysis data, parameter estimation of probabilistic distribution model, and goodness-of-fit test for model parameter and probabilistic distribution model have to be performed in sequence. In this paper, the probabilistic distribution model's geotechnical properties of Songdo area in Incheon are estimated by the above proposed procedure. Also, the coefficient of variation (COV) representing the variability of geotechnical properties is determined for several geotechnical properties. Reliable probabilistic distribution model and COV of geotechnical properties can be used for probability-based design procedure and reasonable choice of design value in deterministic design method.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.123-133
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• 2003

A practical statistical approach that can be used to model various sources of uncertainty systematically is presented in the context of reliability analysis of slope stability. New expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. The stochastic nature of seismic loading is studied by generating a large series of hazard-compatible artificial motions, and by using them in subsequent response analyses. The analyses indicate that in a seismically less active region such as the Korean Peninsular, a moderate variability in soil properties has an effect as large as the characterization of earthquake hazard on the computed risk of slope failure and excessive slope deformations.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.134-137
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• 2008

• 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.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.85-110
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• 2008

Ground investigation data to be used as a basis for geotechnical analysis and foundation design are usually troubled with large uncertainty, due to natural variability and limited number of data. Statistical methods can be a rational tool for handling such uncertain ground data, in particular with a view to the selection of characteristic values for estimating ground design parameters used in design. The characteristic values of soil properties for use in geotechnical design have oftenly based on not only a subjective judgment but also engineer's past acumulated experience. This paper discussed some statistical methods which can handle such intrinsic ground uncertainty data with a case design in a rational manner.

• Journal of the Korean Geotechnical Society
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• v.33 no.5
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• pp.25-35
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• 2017

Liquefaction is one of the major seismic damage, and several methods have been developed to evaluate the possibility of liquefaction. Recently, a probabilistic approach has been studied to overcome the drawback of deterministic approaches, and to consider the uncertainties of soil properties. In this study, the spatial variability of cone penetration resistance was evaluated using CPT data from three locations having different variability characteristics to perform the probabilistic analysis considering the spatial variability of soil properties. Then the random fields of cone penetration resistance considering the spatial variability of each point were generated, and a probabilistic analysis of liquefaction induced settlement was carried out through CPT-based liquefaction evaluation method. As a result, the uncertainty of soil properties can be overestimated when the spatial variability is not considered, and significant probabilistic differences can occur up to about 30% depending on the allowable settlement.

• Geomechanics and Engineering
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• v.17 no.6
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• pp.565-572
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• 2019

Spatial variability is an inherent characteristic of soil, and auto-correlation length (ACL) is a very important parameter in the reliability or probabilistic analyses of geotechnical engineering that consider the spatial variability of soils. Current methods for estimating the ACL need a large amount of laboratory or in-situ experiments, which is a great obstacle to the application of random field theory to geotechnical reliability analysis and design. To estimate the ACL reasonably and efficiently, we propose a micro-structure based numerical simulation method. The quartet structure generation set algorithm is used to generate stochastic numerical micro-structure of soils, and scanning electron microscope test of soil samples combined with digital image processing technique is adopted to obtain parameters needed in the QSGS algorithm. Then, 2-point correlation function is adopted to calculate the ACL based on the generated numerical micro-structure of soils. Results of a case study shows that the ACL can be estimated efficiently using the proposed method. Sensitivity analysis demonstrates that the ACL will become stable with the increase of mesh density and model size. A model size of $300{\times}300$ with a grid size of $1{\times}1$ is suitable for the calculation of the ACL of clayey soils.