• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1425-1432
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• 2010

In large construction site, although soil conversion factor is so significant to preliminary design, operation design and calculating the cost of construction that it is important to take reasonable estimation and application, the standard of soil conversion factor for weathered ground doesn't clearly suggested yet. So in this study, at first we obtain the image using DSLR - high resolution camera and Laser scanner in the Haeng-Bok city constructin site, then analysis the ratio of soil and rock using various image processing method(Sobel method, Laplace method, Highpass filter, Hue and Saturation analysis). Mutual comparation with the result of image processing analysis and manual segmentation of 5case image in the cad. As a result, best image processing method was different for each case. In case of high propotion of rock, Laplace was best and in case of high propotion of soil, Highpass was best, and mixed case Laplace was best.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.35-41
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• 2003

Rock mass characterization is an integral part of rock engineering design. Much of the information for rock mass characterization comes from field fracture mapping and data collecting. This paper describes two technologies that can be used to assist with the field mapping and data collecting activities associated with rock mass characterization: digital image processing and 3D laserscanning. The basis for these techniques is described, as well as the results of field case studies and an analysis of the error in estimating fracture orientation.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Tunnel and Underground Space
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• v.25 no.2
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• pp.144-154
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• 2015

Mechanical energy is accumulated in the object when stress is exerted on rock specimens, and the failure is occurred when the stress is larger than critical stress. The accumulated energy is emitted as various forms including physical deformation, light, heat and sound. Uniaxial compression strength test and point load strength test were carried out in low temperature environment, and thermal variation of rock specimens were observed and analyzed quantitatively using thermal infrared camera images. Temperature of failure plane was increased just before the failure because of concentration of stress, and was rapidly increased at the moment of the failure because of the emission of thermal energy. The variations of temperature were larger in diorite and basalt specimens which were strong and fresh than in tuff specimens which were weak and weathered. This study can be applied to prevent disasters in rock slope, tunnel and mine in cold regions and to analyze satellite image for predicting earthquake in cold regions.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1205-1210
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• 2005

The behavior of rock mass and solute(e.g. groundwater, radioactivity) flow in fractured rock can be directly influenced by joint roughness. The characteristics of joint roughness is also a main factor for the rock classification(e.g. RMR, Q system) which is usually used in tunnel design. Nevertheless, most of JRC estimation has been carried out only by the examination with the naked eye. This JRC estimation has a lack of objectivity because each investigator judges JRC by his subjective opinion. Therefore, it will be desirable that the assessment of JRC is performed by a numerical analysis which can give a quantitative value corresponding to the characteristics of a roughness curve. Meanwhile, roughness curves for joint surfaces which are observed in drill cores have been obtained only along linear profiles. Although roughness curves are measured in the same joint surface, they can frequently show diverse aspects in a standpoint of roughness characteristics. If roughness curves can be measured along the elliptical circumferences of joint surfaces from core scanning images or Televiewer images, they will certainly be more comprehensive than those measured along linear profiles for roughness characteristics of joint surfaces. This study is focus on dealing with (1) extracting automatically roughness curves from core scan image or Televiewer image, (2) improving the accuracy of quantitative assessment of JRC using fractal dimension concept.

• Tunnel and Underground Space
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• v.6 no.3
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• pp.197-208
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• 1996

Due to the constraints in pre site-investigation for tunnel, it is essential to redesign the support structures suitable for rock mass conditions such as rock strength, ground water and discontinuity conditions for safe tunnel construction. For the selection of optimum support, it is very important to carry out the rock mass classification and in-situ measurement in tunnelling. In this paper, in a mountain tunnel designed by NATM in hard rock, the selectable system for optimum support has been studied. The tunnel is situated at Chun-an in Kyungbu highspeed railway line with 2 lanes over a length of 4, 020 m and a diameter of 15 m. The tunnel was constructed by drill & blasting method and long bench cut method, designed five types of standard support patterns according to rock mass conditions. In this tunnel, face mapping based on image processing of tunnel face and rock mass classification by RMR carried out for the quantitative evaluation of the characteristics of rock mass and compared with rock mass classes in design. Also, in-situ measurement of convergence and crown settlement conducted about 30 m interval, assessed the stability of tunnel from the analysis of monitoring data. Through the results of rock mass classification and in-situ measurement in several sections, the design of supports were modified for the safe and economic tunnelling.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2000.09a
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• pp.47-55
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• 2000

Non-linear behavior of rock under compression can be predicted by a crack model. Crack growth in rock renders rock anisotropic. The degree of anisotropy is explained in terms of elastic moduli as a function of load level. In this study, we calculate the changes of elastic moduli due to crack growth numerically by using a crack model and compare these values with experimental results obtained from the measurement of ultrasonic wave velocities. Image processing technique is used to obtain the initial crack information needed for the numerical calculation of elastic moduli.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.163-167
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• 2003

As digital photogrammetry can acquire much three-dimensional data quickly and exactly in equal accuracy, and it has advantage that can use this in modelling, it's practical use possibility is increased in various field by collection method of data for GIS. In this study, it was intended to create 3D image that has coordinate system, and use in acquisition of position information for object. And, it was applied to discontinuities extraction and measurement of rock slope for practical use of three-dimensional image and examination of measurement accuracy. Through this, it is inspected the possibility of three-dimensional image creation and the acquisition of space information.

• Tunnel and Underground Space
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• v.10 no.3
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• pp.301-308
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• 2000

Non-linear behavior of rock under compression can be predicted by a crack model. Crack growth in rock renders rock anisotropic. The degree of anisotropy is explained in terms of elastic moduli as a function of load level. In this study, we calculate the changes of elastic moduli due to crack growth numerically by using a crack model and compare these values with experimental results obtained from the measurement of ultrasonic wave velocities. Image processing technique is used to obtain the initial crack information needed for the numerical calculation of elastic moduli.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.05a
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• pp.168-169
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• 2005