• Tunnel and Underground Space
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• v.10 no.1
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• pp.45-58
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• 2000

Cyclic shear test system, which is capable of measuring flow rate inside rock joint, was established to investigate the hydraulic behavior of rough rock joints under various loading conditions. Laboratory hydraulic tests during compression and shear were conducted for artificial rough rock joints. Prior to tests, aperture characteristics of specimens were examined by measuring surface topography. Permeability changes under compression were well approximated with several hydraulic model. Hydraulic behavior conformed to dilation characteristics in the first stage, and permeability increased with increase of dilation. As the shear displacement progressed, flow rate became somewhat constant due to gouge production and offset of apertures. Hydraulic behavior under cyclic shear loading was also influenced by the degradation of asperities and gouge production. In addition. the relation between hydraulic aperture and mechanical aperture under compression and shear loading was investigated and compared.

• Geotechnical Engineering
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• v.13 no.2
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• pp.77-90
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• 1997

A resonable and economical method which can predict permeability of rock mass in underground is needed to overcome the uncertainty of groundwater behavior. For this par pose, one prediction method of permeability has been studied. The artificial neural networks model using error back propagation algorithm, . one of the teaching techniques, is utilized for this purpose. In order to verify the applicability of this model, in-situ permeability results are simulated. The simulation results show the potentiality of utilizing the neural networks for effective permeability prediction of rock mass.

• Journal of fish pathology
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• v.34 no.2
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• pp.243-248
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• 2021

Offshore wind power generation is an energy generation field that is rapidly developing owing to the increasing demand for clean energy. However, the physiological response of fish to the underwater noise generated during construction or operation of wind turbines is unclear. We confirmed the effects of sound pressures of 125, 135, 145, and 155 dB/µPa, including 140 dB/µPa (the standard sound pressure for noise damage recognition in South Korea), through serum analysis in rock bream (Oplegnathus fasciatus). High mortality induced by reduced immunity through artificial infection after stimulation was confirmed. These results suggest that rock bream is negatively affected by the noise generated during the construction of offshore wind power plants.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 1999.03a
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• pp.1-7
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• 1999

터널은 일종의 지하구조물로서 지형, 지질, 기후 및 기상 등과 같이 영향을 미칠 수 있는 주변환경이 매우 다양할 뿐만 아니라 예기치 못한 외력이 작용하여 구조물에 중대한 문제를 유발할 수도 있는데, 현재 상황으로는 여러 안전진단보고서에서 볼 수 있는 바와 같이 터널에 발생한 일련의 변상현상을 분석하여 그 원인을 규명하고 적절한 보수보강대책을 마련함으로써 예상치 못한 변상에 대한 터널 안전성을 확보하는 작업에 초점이 맞추어지고 있다. (중략)

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.03a
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• pp.25-40
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• 2008

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 1995.03a
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• pp.108-108
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• 1995

There are always some difficulties to discriminate artificial exlposions from micro-earthquakes, furthermore more difficulties to identify and determine decoupled explosions and/or multiple explosions from micro-earthquakes. In this study we use the synthetic seismogram of the in homogeneous models between the source and the observation station in order to find the source effect of the geological environment. We have found some source characteristics of the air-filled and/or water-filled cavity that we can hardly see P-n and S- waves arrivals and that the high frequency coda waves are well observed compared to the coupled explosions or earthquakes.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.274-274
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• 1998

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.311.2-311
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• 1999

• Structural Engineering and Mechanics
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• v.74 no.3
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• pp.407-423
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• 2020

The pre-peak cyclic shear mechanism of two-order asperity degradation of rock joints in the direct shear tests with static constant normal loads (CNL) are investigated using experimental and numerical methods. The laboratory testing rock specimens contains the idealized and regular two-order triangular-shaped asperities, which represent the specific geometrical conditions of natural and irregular waviness and unevenness of rock joint surfaces, in the pre-peak cyclic shear tests. Three different shear failure patterns of two-order triangular-shaped rock joints can be found in the experiments at constant horizontal shear velocity and various static constant normal loads in the direct and pre-peak cyclic shear tests. The discrete element method is adopted to simulate the pre-peak shear failure behaviors of rock joints with two-order triangular-shaped asperities. The rock joint interfaces are simulated using a modified smooth joint model, where microscopic scale slip surfaces are applied at contacts between discrete particles in the upper and lower rock blocks. Comparing the discrete numerical results with the experimental results, the microscopic bond particle model parameters are calibrated. Effects of cyclic shear loading amplitude, static constant normal loads and initial waviness asperity angles on the pre-peak cyclic shear failure behaviors of triangular-shaped rock joints are also numerically investigated.