• Steel and Composite Structures
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• v.26 no.2
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• pp.241-253
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• 2018

Mode II fracture toughness, $K_{IIC}$, of single-ply triaxially woven fabric (TWF) composite due to tow waviness and anisotropy effects were numerically and experimentally studied. The numerical wavy beam network model with anisotropic material description denoted as TWF anisotropic was first validated with experimental Mode II fracture toughness test employing the modified compact tensile shear specimen configuration. 2D planar Kagome and TWF isotropic models were additionally constructed for various relative densities, crack lengths, and cell size parameters for examining effects due to tow waviness and anisotropy. $K_{IIC}$ generally increased with relative density, the inverse of cell size, and crack length. It was found that both the waviness and anisotropy of tow inflict a drop in $K_{IIC}$ of TWF. These effects were more adverse due to the waviness of tow compared to anisotropy.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Journal of the Korean Society of Groundwater Environment
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• v.4 no.3
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• pp.111-115
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• 1997

Discrete fracture model has become one of the alternatives for the classical continuum model to simulate the irregular aspects of the fluid flow and the solute transport in fractured rocks. It is based on the assumptions that the discharge in a single fracture is proportional to the cube of the aperture and the fractured rock can be represented by the statistical assemblage of such single fractures. This study is intended to evaluate the effect of the fracture junction on the cubic law. Numerical solution of flow in junction system was obtained by using the Boundary-Fitted Coordinate System (BFCS) method. Results with different intersection angles in crossing fractures show that the geometry of the junction affects the discharge pattern under the same simulation conditions. Therefore, strict numerical and experimental examinations on this subject are required.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.326-326
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• 2016

The fault can be defined, in a geological context, as a rupture plane showing a significant displacement generated in the case that the local tectonic stress exceeds a threshold of rupture along a particular plane in a rock mass. The hydrogeological properties of this fault can be varied with the spatial distribution and the connectivity of void spaces in a fault. When the formation of fault includes the process of the creation and the destruction of void spaces, a complex relation between the displacement along the fault and the variation of void spaces. In this study, the variation of flow with the geometrical characteristics of the fault is simulated and analyzed by using the three-dimensional discrete fracture network model. Three different geometrical characteristics of the faults are considered in this study: 1) simple hydraulic conductive plane, 2) damaged zone, and 3) relay structure of faults.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.668-669
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• 2008

In this paper, we introduce various coarse-grained elastic network modeling (ENM) techniques as a novel computational method for simulating atomic scale dynamics in macromolecules including DNA, RNA, protein, and polymer. In ENM, a system is modeled as a spring network among representative atoms in which each linear elastic spring is well designed to replace both bonded and nonbonded interactions among atoms in the sense of quantum mechanics. Based on this simplified system, a harmonic Hookean potential is defined and used for not only calculating intrinsic vibration modes of a given system, but also predicting its anharmonic conformational change, both of which are strongly related with its functional features. Various nano and bio applications of ENM such as fracture mechanics of nanocomposite and protein dynamics show that ENM is one of promising tools for simulating atomic scale dynamics in a more effective and efficient way comparing to the traditional molecular dynamics simulation.

• Journal of the Korean GEO-environmental Society
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• v.25 no.5
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• pp.15-19
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• 2024

This study aimed to develop high-efficiency grouting techniques under deep-depth conditions by experimentally verifying the applicability of various injection materials. Particle size analysis and injection model experiments were conducted with Ordinary Portland Cement (OPC) and Micro Cement (MC) to evaluate the injection performance of each material. Using Barton's Cubic Network theory, the rock fracture spacing was calculated for domestic deep-depth standards, specifically below 40 meters underground. The analysis of particle size passability under selected conditions showed that MC could pass through the rock fracture gaps, while OPC could not. According to the results of the injection model experiments using experimental devices and area calculation software, OPC failed in injection due to its larger particle size, whereas MC was capable of injection even under high-viscosity conditions. Based on these findings, the study quantitatively and visually derived the applicability of grouting materials under deep-depth conditions, and high-viscosity MC material is expected to be effective for waterproofing enhancement in deep-depth rock fracture surfaces.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.09a
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• pp.32-35
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• 2002

In order to characterize the migration of DNAPL in rock fractures, the dynamic macromodified invasion percolation (DMMIP) model, that is able to reflect the viscous force of groundwater in a fracture network, is suggested. DMMIP simulations are verified against the laboratory expenments, which shows a good qualitative and quantitative agreement.

• Water Engineering Research
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• v.2 no.2
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• pp.123-138
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• 2001

An injection experiment was carried ut to investigate the pressure domain within which hydromechanical coupling influences considerably the hydrologic behavior of a granite rock mass. The resulting database is used for testing a numerical model dedicated to the analysis of such hydromechanical interactions. These measurements were performed in an open hole section, isolated from shallower zones by a packer set at a depth of 275 m and extending down to 840 m. They consisted in a series of flow meter injection tests, at increasing injection rates. Field results showed that conductive fractures from a dynamic and interdependent network, that individual fracture zones could not be adequately modeled as independent systems, that new fluid intakes zones appeared when pore pressure exceeded the minimum principal stress magnitude in that well, and that pore pressures much larger than this minimum stress could be further supported by the circulated fractures. These characteristics give rise to the question of the influence of the morphology of the natural fracture network in a rock mass under anisotropic stress conditions on the effects of hydromechanical couplings.

• Journal of Mechanical Science and Technology
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• v.19 no.7
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• pp.1393-1404
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• 2005

Fatigue crack growth and life have been estimated based on established empirical equations. In this paper, an alternative method using artificial neural network (ANN) -based model developed to predict fatigue damages simultaneously. To learn and generalize the ANN, fatigue crack growth rate and life data were built up using in-plane bending fatigue test results. Single fracture mechanical parameter or nondestructive parameter can't predict fatigue damage accurately but multiple fracture mechanical parameters or nondestructive parameters can. Existing fatigue damage modeling used this merit but limited real-time damage monitoring. Therefore, this study shows fatigue damage model using backpropagation neural networks on the basis of X -ray half breadth ratio B / $B_o$, fractal dimension $D_f$ and fracture mechanical parameters can estimate fatigue crack growth rate da/ dN and cycle ratio N / $N_f$ at the same time within engineering limit error ($5\%$).

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.354-366
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• 2020

This work mainly focuses on determination of the fully plastic J-integral solutions for welded center cracked plates subjected to remote tension loading. Detailed three-dimensional elasticeplastic Finite Element Analyses (FEA) were implemented to compute the fully plastic J-integral along the crack front for a wide range of crack geometries, material properties and weld strength mismatch ratios for 900 cases. According to the database generated from FEA, Back-propagation Neural Network (BPNN) model was proposed to predict the values and distributions of fully plastic J-integral along crack front based on the variables used in FEA. The determination coefficient R2 is greater than 0.99, indicating the robustness and goodness of fit of the developed BPNN model. The network model can accurately and efficiently predict the elastic-plastic J-integral for weld centerline crack, which can be used to perform fracture analyses and safety assessment for welded center cracked plates with varying strength mismatch conditions under uniaxial loading.