• The Journal of Engineering Geology
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• v.19 no.3
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• pp.351-363
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• 2009

Two orthogonal joint sets develop well only in sandstone beds in the sandstone-mudstone sequences of Gumi and Dasa outcrops within Cretaceous Gyeongsang Basin. And various joint data are similar in the beds of the same thickness in both outcrops, meaning that the joint sets were homogeneously produced by extensional deformation in the same regional stress field. Most of joints in the sandstone beds are orthogonal to, and confined by bed boundaries, which are believed to be formed by hydrofracturing during consolidation after burial. Two orthogonal joint sets are considered to be almost coeval on the basis of mutual abutting relationship which makes up fracture grid-lock and a product of rapid switching of ${\sigma}_2$ and ${\sigma}_3$ axes with constant ${\sigma}_1$ direction oriented to vertical. The joint sets in the sandstone beds show planar surfaces, parallel orientations and regular spacing, with joint spacing linearly proportional to bed thickness. The spacing distributions of the joints seem to correspond to log-normal to almost normal distribution in most of the beds. But multilayer joints do not display regular spacing and dominant size. Either joint set in this study is characterized by a high level of joint density and a saturated spacing distribution as indicated by the mode/mean ratio values and the Cv(coefficient of variance) values. Joint aperture tends to increase with the vertical length of the joints controlled by bed thickness.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.187-194
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• 2012

The strength of particle-reinforced metal matrix composites is, in general, known to be increased by the geometrically necessary dislocations punched around a particle that form during cooling after consolidation because of coefficient of thermal expansion (CTE) mismatch between the particle and the matrix. An additional strength increase may also be observed, since another type of geometrically necessary dislocation can be formed during extensive deformation as a result of the strain gradient plasticity due to the elastic-plastic mismatch between the particle and the matrix. In this paper, the magnitudes of these two types of dislocations are calculated based on the dislocation plasticity. The dislocations are then converted to the respective strengths and allocated hierarchically to the matrix around the particle in the axisymmetric finite-element unit cell model. The proposed method is shown to be very effective by performing finite-element strength analysis of $SiC_p$/Al2124-T4 composites that included ductile failure in the matrix and particlematrix decohesion. The predicted results for different particle sizes and volume fractions show that the length scale effect of the particle size obviously affects the strength and failure behavior of the particle-reinforced metal matrix composites.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.1
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• pp.65-72
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• 1983

Dry sand specimens for both normally consolidated and overconsolidated triaxial compression tests were prestressed on the path with five different coefficients of earth pressure 1.0, 3/4, 0.55, $K_0$ and 1/3. Deformation resistance of normally consolidated sand increased with increasing the initial stress for all coefficients of earth pressure during consolidation, and the deformation modulus at a certain initial stress showed a tendency to increase with increasing the coefficient of earth pressure. And deformation moduli($E_i$, $E_{50}$), were found to be proportional to the $n_{th}$ power of initial stresses[${\sigma}_{m0}{^{\prime}}$, ${\sigma}_{10}{^{\prime}}$, ${\sigma}_{30}{^{\prime}}$, $({\sigma}_1-{\sigma}_3)_0$] for both isotropically and anisotropically normally consolidated samples, where n varied from 0.37 to 0.92. Overconsolidated sand with the higher overconsolidation ratio showed the higher deformation modulus. It is concluded that the $K_0$-anisotropically consolidated triaxial compression test is necessary to obtain the more accurate value of in-situ deformation modulus.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.