• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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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.

• 한국지반공학회논문집
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• 제25권6호
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• pp.73-88
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• 2009

지반의 변동성은 많은 독립된 불확실성 요소로부터 발생하는 복잡한 요소이며, 지반의 변동성은 주로 내재적 변동성과 측정오차에 의해서 발생한다. 본 논문에서는 지반의 변동성을 평가하는 지표로 사용되는 변동계수를 송도지역의 지반정수 및 지층에 대하여 산정하였다. 지반정수의 변동성은 지층 및 흙의 종류에 영향을 받으므로 지층은 매립층과 퇴적층, 흙의 종류는 점토와 실트로 구분하여 각각의 변동계수를 산정하였다. 퇴적층과 점토의 변동계수가 매립층과 실트에 비하여 작은 것으로 분석되었다. 또한, 풍화가 많이 진행된 암반과 토사가 신선한 암반과 풍화암에 비하여 상대적으로 큰 변동계수를 보이는 것으로 나타났다. 본 논문에서 제시한 송도지역의 변동계수는 신뢰성해석의 자료로 사용될 수 있을 것으로 판단된다.

• 한국지반공학회논문집
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• 제35권3호
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• pp.37-46
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• 2019

최근 다른 현장시험에 비하여 비교적 정확성이 높은 CPT 기반 액상화 평가법의 사용이 증가하고 있다. CPT 기반 액상화 평가는 다양한 흙의 특성을 예측하고 이를 액상화 평가에 활용할 수 있다. 특히, 세립분 함량은 CPT 기반 액상화 평가에서 중요한 입력 변수 중 하나로 이에 대한 정확한 예측식의 사용 및 예측 변동성을 정량적으로 파악하는 것은 매우 중요하다. 본 연구에서는 2017년 포항지진 시 액상화 현상이 관측된 지점에서 수행된 CPT 자료를 이용하여 기존 세립분 함량 예측식들의 오차를 분석하고 포항지역에 적합한 세립분 함량 예측식을 선정하였다. 또한, 지반의 고유한 변동성을 분석하고 CPT의 측정오차, 선정된 예측식에 대한 변환 불확실성을 고려한 세립분 함량의 예측 변동성을 정량적으로 평가하였다.

• 한국지반환경공학회 논문집
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• 제25권11호
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• pp.13-24
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• 2024

The propagation of elastic waves in soil is crucial in geotechnical and seismic engineering. Although soil is often assumed homogeneous, natural geomaterials like soil and rock possess inherent heterogeneity. This study uses FLAC 2D finite difference software to simulate wave propagation under different spatial variability parameters. Random field models and Monte Carlo methods were employed to generate random field data for soil parameters, reflecting the actual variability of soil. The study analyzes the effects of different correlation lengths, variability parameters, and saturation on the propagation characteristics of elastic waves, including wave velocity, amplitude attenuation, and waveform changes. Results show that wave propagation is most sensitive to elastic modulus variability, followed by porosity, while Poisson's ratio has minimal impact. Due to the variability of the elastic modulus, wave propagation time increases with increasing variability coefficient and correlation length. The peak amplitude decreases significantly, and the attenuation mean decreases while the variability of attenuation increases with increasing variability coefficient. Additionally, increasing soil saturation in heterogeneous soils leads to a decrease in wave velocity and an increase in attenuation. These findings contribute to a better understanding of elastic wave propagation in heterogeneous soils and improving design reliability.

• Geomechanics and Engineering
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• 제17권1호
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• pp.31-45
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• 2019

In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of $FLAC^{3D}$. The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.

• 환경영향평가
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• 제9권3호
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• pp.185-202
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• 2000

Uncertainty and variability in Life Cycle Assessment(LCA) have been significant key issues in LCA methodology with techniques in other research area such as social and political science. Variability is understood as stemming from inherent variations in the real world, while uncertainty comes from inaccurate measurements, lack of data, model assumptions, etc. Related articles in this issues were reviewed for classification, distinguish and elaboration of probabilistic/stochastic health risk analysis application in LCA. Concept of focal zone, streamlining technique, scenario modelling and Monte Carlo/Latin Hypercube risk analysis were applied to the uncertainty/variability analysis of health risk in LCA. These results show that this general framework of multi-disciplinary methodology between probabilistic health risk assessment and LCA was of benefit to decision making process by suppling information about input/output data sensitivity, health effect priority and health risk distribution. There should be further research needs for case study using this methodology.

• Geomechanics and Engineering
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• 제17권6호
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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.

• Geomechanics and Engineering
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• 제24권2호
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• pp.167-178
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• 2021

Spatial variability is an inherent uncertainty of soil properties. Current reliability analyses generally incorporate random field theory and Monte Carlo simulation (MCS) when dealing with spatial variability, in which the computational efficiency is a significant challenge. This paper proposes a KL-FORM algorithm to improve the computational efficiency. In the proposed KL-FORM, Karhunen-Loeve (KL) expansion is used for discretizing random fields, and first-order reliability method (FORM) is employed for reliability analysis. The KL expansion and FORM can be used in conjunction, through adopting independent standard normal variables in the discretization of KL expansion as the basic variables in the FORM. To illustrate the effectiveness of this KL-FORM, it is applied to a case study of a strip footing in spatially variable unsaturated soil under rainfall, in which the bearing capacity of the footing is computed by numerical simulation. This case study shows that the KL-FORM is accurate and efficient. The parametric analyses suggest that ignoring the spatial variability of the soil may lead to an underestimation of the reliability index of the footing.

• The Journal of the Acoustical Society of Korea
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• 제25권1E호
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• pp.1-7
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• 2006

This study examines the reflection characteristic of a thin transition layer of the ocean bottom showing variability with respect to depth. In order to model the surficial sediment simply, we reduce the Biot model to the depth dependent wave equation for the pseudo fluid using the fluid approximation (weak frame approximation). From the reduced equation, the difference between the inherent frequency dependency of the reflection and the frequency dependency resulting from a thin transition layer is investigated. Using Tang's depth porosity profile model of the surficial sediment [D. Tang et al., IEEE J. Oceanic Eng., vol.27(3), 546-560(2002)], we numerically simulated the reflection loss and investigated the contribution from both frequency dependencies. In addition, the effects of different sediment type and varying depth structure of the sediment are discussed.

• 대한의용생체공학회:의공학회지
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• 제45권4호
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• pp.179-186
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• 2024

This paper investigates the impact of Riemannian Procrustes Analysis (RPA) on enhancing the classification performance of SPD-Net when applied to EEG signals across different sessions and subjects. EEG signals, known for their inherent individual variability, are initially transformed into Symmetric Positive Definite (SPD) matrices, which are naturally represented on a Riemannian manifold. To mitigate the variability between sessions and subjects, we employ RPA, a method that geometrically aligns the statistical distributions of these matrices on the manifold. This alignment is designed to reduce individual differences and improve the accuracy of EEG signal classification. SPD-Net, a deep learning architecture that maintains the Riemannian structure of the data, is then used for classification. We compare its performance with the Minimum Distance to Mean (MDM) classifier, a conventional method rooted in Riemannian geometry. The experimental results demonstrate that incorporating RPA as a preprocessing step enhances the classification accuracy of SPD-Net, validating that the alignment of statistical distributions on the Riemannian manifold is an effective strategy for improving EEG-based BCI systems. These findings suggest that RPA can play a role in addressing individual variability, thereby increasing the robustness and generalization capability of EEG signal classification in practical BCI applications.