• Water for future
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• v.50 no.5
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• pp.44-48
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• 2017

• Tunnel and Underground Space
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• v.21 no.2
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• pp.117-127
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• 2011

The purpose of this study is to demonstrate the effect of in-situ rock stresses on the deformability and permeability of fractured rocks. Geological data were taken from the site investigation at Forsmark, Sweden, conducted by Swedish Nuclear Fuel and Waste Man-agement Company (SKB). A set of numerical experiments was conducted to determine the equivalent mechanical properties (essentially, elastic moduli and Poisson's ratio) and permeability, using a Discrete Fracture Network-Discrete Element Method (DFN-DEM) approach. The results show that both mechanical properties and permeability are highly dependent on stress because of the hyperbolic nature of the stiffness of fractures, different closure behavior of fractures, and change of fluid pathways caused by deformation. This study shows that proper characterization and consideration of in-situ stress are important not only for boundary conditions of a selected site but also for the understanding of the mechanical and hydraulic behavior of fractured rocks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.347-360
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• 1998

Numerical simulations for fluid flow and solute transport in a fracture rock masses are performed by using a transient flow model, which is based on the three-dimensional stochastic and discrete fracture network model (DFN model) and is coupled hydraulic model with mechanical model. In the numerical simulations of the solute transport, we used to the particle following algorithm which is similar to an advective biased random walk. The purpose of this study is to predict the response of the tracer test between two deep bore holes (GPK1 and GPK2) implanted at Soultz sous Foret in France, in the context of the geothermal researches.l The data sets used are obtained from in situcirculating experiments during 1995. As the result of the transport simulation, the mean transit time for the non reactive particles is about 5 days between two bore holes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Tunnel and Underground Space
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• v.21 no.2
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• pp.128-137
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• 2011

The shut-in pressure calculated in common hydrofracturing test for vertical borehole equals generally to the minimum horizontal principal stress, so it should be considered as an essential parameter for determining the in-situ stress regime around the rock mass. It shows usually an ambiguous value in pressure-time history curves, however, because of the relationship between the behavior of hydraulic fractures and the condition of remote stress regime. In this study, a series of numerical analyses have been carried out to compare several methods for determining the shut-in pressure during hydrofracturing. The hydraulic-mechanical coupling has been applied to numerical analysis for simulating the fracture propagation by hydraulic pressure, and the different discontinuity geometry has been considered in numerical models to examine the effect of numerical element shape on fracture propagation pattern. From the numerical simulations with the four different discontinuity geometries, it was revealed that the shut-in pressure obtained from graphical methods rather than statistical method was relatively small. Consequently a care should be taken in selecting a method for determining the shut-in pressure when a stress anomaly around borehole and a fracture propagation with complicate mechanism are considered.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2004.11a
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• pp.297-302
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• 2004

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2012.05a
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• pp.317-318
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• 2012

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.532-538
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• 2002

This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms such as crack opening and closure, rubbing, fretting etc. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could accounted for the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.