• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.143-149
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• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.455-463
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• 2018

This study developed a method to quantitatively measure the size of cracks in concrete using X-ray CT images. We prepared samples with a diameter of 50 mm and a length of 100 mm by coring cracked concrete block that was obtained by chipping. We used a micro-focus X-ray CT, then applied the 3DMA method (3 Dimensional Medial axis Analysis) to the 3D CT images to find effective parameters for damage assessment. Finally, we quantitatively assessed the damage based on sample locations, using the damage assessment parameter. Results clearly show that the area near the chipping surface was damaged to a depth of 3 cm. Furthermore, X-ray methods can be used to evaluate the porosity index, burn number, and medial axis, which are used to estimate the damage to the area near the chipping surface.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.121-127
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• 2011

Spatial distribution of void space in concrete materials strongly affects mechanical and physical behaviors. Therefore, the identification of characteristic void distribution helps understand material properties and is essential to estimate the integrity of material performance. The 3D micro CT (X-ray microtomography) is implemented to examine and to quantify the void distribution of a lightweight aggregate concrete using an image analysis technique and probabilistic approach in this study. The binarization and subsequent stacking of 2D cross-sectional images virtually create 3D images of targeting void space. Then, probability distribution functions such as two-point correlation and lineal-path functions are applied for void characterization. The lightweight aggregates embedded within the concrete are individually analyzed to construct the intra-void space. Results shows that the low-order probability functions and the density distribution based on the 3D micro CT images are applicable and useful methodology to characterize spatial distribution of void space and constituents in concrete.

• 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.

• 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.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.542-557
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• 2019

The behavior of rock mass is influenced by its microscopic feature of internal structure generating from forming and metamorphic process. This study investigated a new methodology for characterization of rock based on the X-ray CT (computed tomography) images reflecting the spatial distribution characteristics of internal constituent materials. The X-ray image based analysis is capable of quantification of heterogeneity and anisotropy of rock fabric, size distribution and shape parameter analysis of rock mineral grains, fluid flow simulation based on pore geometry image and roughness evaluation of unexposed joint surface which are hardly acquired by conventional rock testing methods.

• 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.

• Journal of the Korean Geotechnical Society
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• v.39 no.8
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• pp.17-28
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• 2023

Laboratory-scale hydraulic fracturing experiments were conducted on granite specimens at various viscosities and injection rates of the fracturing fluid. A series of cross-sectional computed tomography (CT) images of fractured specimens was obtained via a three-dimensional X-ray CT imaging method. Pixel-level fracture segmentation of the CT images was conducted using a convolutional neural network (CNN)-based Nested U-Net model structure. Compared with traditional image processing methods, the CNN-based model showed a better performance in the extraction of thin and complex fractures. These extracted fractures extracted were reconstructed in three dimensions and morphologically analyzed based on their fracture volume, aperture, tortuosity, and surface roughness. The fracture volume and aperture increased with the increase in viscosity of the fracturing fluid, while the tortuosity and roughness of the fracture surface decreased. The findings also confirmed the anisotropic tortuosity and roughness of the fracture surface. In this study, a CNN-based model was used to perform accurate fracture segmentation, and quantitative analysis of hydraulic stimulated fractures was conducted successfully.

• 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.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.249-255
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• 2015

In this study, through the additional information of the PET / CT images by utilizing the basic data of TPS clinical application on the basis of the image re-forming synthetic function, the True-D technology and MIM software for continued research and development in combination, based on the combination-work between the respective images, reducing the time and cost of useful reading in clinical wide use of image width, efficient, effective tool for tumor targeting at diagnosis and radiation therapy by use as, by using the precise therapeutic effect determination, the time taken to read in the clinical, unnecessary and expect to a can reduce the additional examination by the creation of tumor patients read reports and PACS such asWe expect to be utilized for compatibility development with other software to evaluate the performance of PET / CT equipment.