• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.227-235
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• 2012

The void distribution in concrete materials strongly affects its material properties. Therefore, the identification of spatial distribution of void is important to understand and estimate material behavior. To examine and quantify the void distribution inside lightweight aggregates, CT(computed tomography) image is used. 3D lightweight aggregate images are generated by stacking of cross-sectional images from CT. Spatial distribution of void of aggregate along the direction is visualized on the sphere using probability distribution function. Stiffness of lightweight aggregate for the directions is also examined. It is confirmed that direction-based probability distribution and stiffness from CT images are effective in characterizing void distributions of aggregates.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.463-469
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• 2016

In this study, a virtual specimen with a linear continuous gradient of void ratio (FGM: Functional Graded Material) is constructed using low-order probability functions of two real cement paste specimens. Two real specimens with difference void ratios are taken from X-ray CT to construct the virtual specimen. A virtual specimen with a gradient void distribution, whose average void ratio is between void ratios of two homogeneous real specimens, is constructed using a stochastic optimization approach. The void ratio distribution is assumed to be linear, and continuously varies in the vertical direction. In this study, a gradient term of void ratio is incorporated into the objective function as well as low-order probability functions from the previous research. To confirm the effect of gradient void distribution on the material response, air permeability is evaluated using finite element analysis. The analysis results are compared with experimental results, and confirm the effect of gradient void distribution on permeability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6A
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• pp.449-456
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• 2011

Spacing factor proposed by Power is a concept of averaging air void spacing composed of geometric models. Thus, there's a limitation on simulating actual air void characteristics in concrete. This study presents SDI(spacing distribution index) to overcome the limitation of spacing factor. SDI is also evaluated through comparing SDI with SF(spacing factor). In this study, it was confirmed that SF decreased due to increasing air-entrainer content but SDI increased. This occurs because SDI is the area of spacing distribution curve and SDI increases with increasing the frequency of spacing. SDI is evaluated to have better coverage below $300{\mu}m$ of SF so that determination of critical point of SDI above 80% of durability index can be easily obtained with more reliability. SDI is the area of spacing distribution curve and reflects actual air void characteristics in concrete. A comparative study of SDI and results of freeze-thaw test will be performed later.

• The Journal of Engineering Geology
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• v.24 no.3
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• pp.353-362
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• 2014

Pore structures in porous rock play an important role in hydraulic & mechanical behaviour of rock. Porosity, size distribution and orientation of pores represent the characteristics of pore structures of porous rock. While effective porosity can be measured easily by conventional experiment, pore size distribution is hard to be quantified due to the lack of corresponding experiment. We assessed pore size distribution of Berea sandstone using X-ray CT image based analysis combined with associated images processing, i.e., image filtering, binarization and skeletonization subsequently followed by the assessment of local thickness and star chord length. The aim of this study is to propose a new and effective way to evaluate pore structures of porous rock using X-ray CT based analysis for pore size distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.141-147
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• 2024

In this study, micro CT images were used to confirm the tensile fracture strength according to the pore distribution characteristics of 3D printed concrete. Unlike general specimens, concrete structures printed by 3D printing techniques have the direction of pores (voids) depending on the stacking direction and the presence of filaments contact surfaces. Accordingly, the pore distribution of 3D printed concrete specimens was analyzed through quantitative and qualitative methods, and the tensile strength by direction was analyzed through a finite element technique. It was confirmed that the pores inside the 3D printed specimen had directionality, resulting in their anisotropic behavior. This study aims to analyze the characteristics of 3D concrete printing specimen and correlate them with simulation-based mechanical properties to improve performance of 3D printed material and structure.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.644-647
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• 2011

지형 및 환경에 따라 해안 사면의 침식, 세굴을 막기 위한 효과 있는 해안구조물의 설치가 필요하다. 본 연구에서는 유체-구조 상호작용을 고려해서 유한요소해석 프로그램인 LS-DYNA를 사용하여 조파 실험모델링을 하고, 해안사석구조물과 상호작용에 의한 유체 흐름을 분석하였다. 사석의 유무와 크기, 개수 등의 변수를 조정하여 서로 다른 4가지 경우에 따른 결과를 비교 분석하였다. 또한 해안사석구조물을 구성하는 투수 콘크리트의 특성 분석을 위하여 CT 이미지를 사용하여 투수 콘크리트의 공극 분포를 관찰하였다. 투수 콘크리트의 특성에 큰 영향을 미치는 공극 분포의 파악을 위하여 확률 분포 함수를 사용하여 투수 콘크리트 공극의 공간적 분포를 분석하였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1992.10a
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• pp.42-47
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• 1992

본 연구에서는 공극관의 실험인자에 대한 특성을 알아보기 위하여 공극관에 입사되는 음파의 파수 k와 공극분포부분들 사이의 거리인 a의 곱으로 나타나는 무차원변수 ka와, 공극관의 단면적에 대한 전체 공극면적의 비로 정의한 공극률을 실험인자로 하여 연구를 진행하였다. 또한 sulivan의 모델을 이용하여 공극관을 모델링하고 이를 컴퓨터를 사용하여 모의 실험을 한 후 실험결과와 비교하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.339-346
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• 2023

The uncertainties of microstructural features affect the properties of materials. Numerous pores that are randomly distributed in materials make it difficult to predict the properties of the materials. The distribution of pores in cementitious materials has a great influence on their mechanical properties. Existing studies focus on analyzing the statistical relationship between pore distribution and material responses, and the correlation between them is not yet fully determined. In this study, the mechanical response of cementitious materials is predicted through an image-based data approach using a convolutional neural network (CNN), and the correlation between pore distribution and material response is analyzed. The dataset for machine learning consists of high-resolution micro-CT images and the properties (tensile strength) of cementitious materials. The microstructures are characterized, and the mechanical properties are evaluated through 2D direct tension simulations using the phase-field fracture model. The attributes of input images are analyzed to identify the spot with the greatest influence on the prediction of material response through CNN. The correlation between pore distribution characteristics and material response is analyzed by comparing the active regions during the CNN process and the pore distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1A
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• pp.31-37
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• 2012

The phase distribution in concrete materials strongly affects its material properties. It is important to identify the spatial distribution of void in concrete because the void in concrete materials affects mechanical behavior and permeability significantly. Therefore, a proper method to describe the void distribution of a material is needed. In this research, CT(computed tomography) is used to examine and to quantify the void distribution of porous concrete specimens. 3D concrete digital specimens are created by subsequent stacking of 2D cross-sectional images from CT. Then, probability distribution functions such as two-point correlation, lineal-path and two-point cluster functions are used for void distribution characterization. It is confirmed that probability distribution functions obtained from CT images are effective in characterizing void distributions including the anisotropy and percolation.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.407-414
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• 2010

Until now, the pore size distribution, PSD, of soil profile has been calculated from soil moisture characteristic data by water release method or mercury porosimetry using the capillary rise equation. But the current methods are often difficult to use and time consuming. Thus, in this work, theoretical framework for an easy and fast technique was suggested to estimate the PSD from unsaturated hydraulic conductivity data in an undisturbed field soil profile. In this study, unsaturated hydraulic conductivity data were collected and simulated by the variation of soil parameters in the given boundary conditions (Brooks and Corey soil parameters, ${\alpha}_{BC}=1-5L^{-1}$, b = 1 - 10; van Genuchten soil parameters, ${\alpha}_{VG}=0.001-1.0L^{-1}$, m = 0.1 - 0.9). Then, $K_s$ (1.0 cm $h^{-1})$ was used as the fixed input parameter for the simulation of each models. The PSDs were estimated from the collected K(h) data by model simulation. In the simulation of Brooks-Corey parameter, the saturated hydraulic conductivity, $K_s$, played a role of scaling factor for unsaturated hydraulic conductivity, K(h) Changes of parameter b explained the shape of PSD curve of soil intimately, and a ${\alpha}_{BC}$ affected on the sensitivity of PSD curve. In the case of van Genuchten model, $K_s$ and ${\alpha}_{VG}$ played the role of scaling factor for a vertical axis and a horizontal axis, respectively. Parameter m described the shape of PSD curve and K(h) systematically. This study suggests that the new theoretical technique can be applied to the in situ prediction of PSD in undisturbed field soil.