• Title/Summary/Keyword: Rock classification

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Study on the Classification of Weak Rock by Test Blast (시험발파에 의한 연약암반 평가에 대한 연구)

  • 선우춘;전양수;천대성;한공창
    • Explosives and Blasting
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    • v.21 no.4
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    • pp.1-10
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    • 2003
  • The classification of weak rocks is normally connected with the rippability classifications. The excavation of rock is frequently carried out by blasting. A classification of the weak rocks by test blasting with small quantity of explosives was attempted in the present study. The crater ratio and blasting constant that resoled from test blasting were used as a e parameter of the classification. The seismic velocity of rock mass and Protodyakonov's index were also applied for the also rock classification.

A Geostatisitical Study Using Qualitative Information for Multiple Rock Classification II. Application (다분적 암반분류를 위한 정성적 자료의 지구통계학적 연구- II. 응용)

  • 유광호
    • Geotechnical Engineering
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    • v.14 no.1
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    • pp.29-36
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    • 1998
  • The application of a multiple rock classification method, which is a generalization of a binary rock classification, is studied in this paper. In particular, this paper shows how to incorporate qualitative data through a case study. The method suggested in this paper can be effectively used for a systematic multiple rock classification such as RMR system developed by Bieniawski. It will be very useful for rock classifications. In addition, it is known that the expected cost of errors can be atopted to indicate how well a investigation plan is made.

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Technical Development for Extraction of Discontinuities in Rock Mass Using LiDAR (LiDAR를 이용한 암반 불연속면 추출 기술의 개발 현황)

  • Lee, Hyeon-woo;Kim, Byung-ryeol;Choi, Sung-oong
    • Tunnel and Underground Space
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    • v.31 no.1
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    • pp.10-24
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    • 2021
  • Rock mass classification for construction of underground facilities is essential to secure their stabilities. Therefore, the reliable values for rock mass classification from the precise information on rock discontinuities are most important factors, because rock mass discontinuities can affect exclusively on the physical and mechanical properties of rock mass. The conventional classification operation for rock mass has been usually performed by hand mapping. However, there have been many issues for its precision and reliability; for instance, in large-scale survey area for regional geological survey, or rock mass classification operation by non-professional engineers. For these reasons, automated rock mass classification using LiDAR becomes popular for obtaining the quick and precise information. But there are several suggested algorithms for analyzing the rock mass discontinuities from point cloud data by LiDAR scanning, and it is known that the different algorithm gives usually different solution. Also, it is not simple to obtain the exact same value to hand mapping. In this paper, several discontinuity extract algorithms have been explained, and their processes for extracting rock mass discontinuities have been simulated for real rock bench. The application process for several algorithms is anticipated to be a good reference for future researches on extracting rock mass discontinuities from digital point cloud data by laser scanner, such as LiDAR.

Suggestion of New Rock Classification Method Using the Existing Classification Method (기존의 암반분류법의 조합에 의한 새로운 암반평가법의 제안)

  • SunWoo Choon;Jung Yong-Bok
    • Explosives and Blasting
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    • v.24 no.1
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    • pp.21-28
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    • 2006
  • Rock mass classification systems such as RMR and Q system have been widely served as a simple empirical approach for the design of various rock mass structures in the stage of site survey as well as under the construction. For the RQD determination, the boring is partially carried out and what is more, the survey boring is not normally carried out under construction. Therefore RQD is frequently determined by empirical method or indirect method. Since it is difficult to determine the discontinuity characteristics such as RQD, spacing, persistence, filling and so on, it is essential to develop suitable and simple systems without drilled core and a cert 없 n number of representative parameters. One of the primary objectives of the classification systems for a practicing engineer has been to make it simple to use as a preliminary design tool for the structures in rock mass. In the present study, the modifications for both the RMR and GSI system are suggested by authors to introduce new classification system as well as to improve the scope of some of the existing classification systems for a practicing engineer.

Classification of Rock Mass on Cutting Slopes in Muakjae, Seoul (서울 무악재 절취사면에서의 암판정 연구)

    • Tunnel and Underground Space
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    • v.9 no.2
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    • pp.158-167
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    • 1999
  • There are substantial difficulties in assessing the volume of soill/rock to be excavated and the cost thereof, which is attributable to the subjective and qualitative methods of rock mass classification prevailing at the moment. This paper intends to introduce more objective and quantitative rock mass classification method easily applicable to the excavation of granites in Muakjae, Seoul. As a result of such study it is proven that Schmidt hammer and point load strength tests are fairly reliable and easily applicable to estimate and quantify uniaxial compressive strength of granitic material in Seoul. In an efforts to confirm the granitic rock mass conditions in 12 meters underground, seismic refraction surveys were made on the top of vertical exposures from where underlying rock mass conditions could be directly inspected. Rock mass boundaries determined by seismic refraction methods were found to agree within a 1m variance with visible differences in rock mass conditions in the vertical exposure beneath the test site. Thus it can be concluded that detailed geotechnical mapping on cutting slopes is a most efficient, dependable and cost-effective technique in assessing likely excavation conditions of shallow granitic mass in Seoul.

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A study on the Correlation Between the Result of Electrical Resistivity Survey and the Rock Mass Classification Values Determined by the Tunnel Face Mapping (전기비저항탐사결과와 터널막장 암반분류의 상관성 검토)

  • Choi, Jai-Hoa;Jo, Churl-Hyun;Ryu, Dong-Woo;Kim, Hoon;Oh, Byung-Sam;Kang, Moon-Gu;Suh, Baek-Soo
    • Tunnel and Underground Space
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    • v.13 no.4
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    • pp.279-286
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    • 2003
  • Many trials to set up the correlation between the rock mass classification and the earth resistivity have been carried out to design tunnel support type based on the interpreted electrical resistivity acquired by surface electrical survey. But it is hard to find reports on the comparison of the real rock support type determined during the excavation with the electrical resistivity by the inversion of the survey data acquired before the tunneling. In this study, the rock mass classification based on the face mapping data and the resistivity inversion data are investigated to see if it is possible to design reliably the rock support type based on the surface electrical survey. To get the quantitative correlation, rock engineering indices such as RCR(rock condition rating), N(Rock mass number), Q-system and RMR(rock mass rating) are calculated. Since resistivity data has low resolution, Kriging method as a post processing technique which minimizes the estimated variance is used to improve resolution. The result of correlation analysis shows that the 2D electrical resistivity survey is appropriate to see the general trend of the geology in the sense of rock type, though there might be some local area where these two factors do not coincide. But the correlation between the result of 3D survey and the rock mass classification turns out to be very high, and then 3D electrical resistivity survey can make it possible to set up more reliable rock support type.

Evaluating the Effectiveness of an Artificial Intelligence Model for Classification of Basic Volcanic Rocks Based on Polarized Microscope Image (편광현미경 이미지 기반 염기성 화산암 분류를 위한 인공지능 모델의 효용성 평가)

  • Sim, Ho;Jung, Wonwoo;Hong, Seongsik;Seo, Jaewon;Park, Changyun;Song, Yungoo
    • 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.

A study on rock mass classification in the design of tunnel using multivariate discriminant analysis (다변량 판별분석을 통한 터널 설계시의 암반분류 연구)

  • Lee, Song;Ahn, Tae Hun;You, Oh Shick
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.6 no.3
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    • pp.237-245
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    • 2004
  • In designing a tunnel, RMR has been widely used to classify rock mass and to decide the support pattern according to the class of rock mass. However, this RMS system can't help relying on the empirical judgment of engineers who use variables which can be obtained only through consideration of the site conditions. In actuality, it is impossible to consider all the rating factors of RMS when using RMR system at the stage of designing. Therefore, in order to confirm possibility of RMR by use of only the quantitative factors for designing, this paper has done discriminant analysis. Rock strength or RQD has high coefficient of correlation with RMR value, and in consideration of the existing standards for rock mass classification, rock intensity and RQD are important factors for classification of rock mass. Through rock mass classification by the existing RMR system and rock mass classification by the discriminant analysis which has considered two variables only, the discriminant analysis using the rock intensity as an independent variable has shown 74.8% accuracy while the discriminant analysis using RQD as an independent variable has shown 74.3% accuracy. In case of the discriminant analysis which has considered both rock intensity and RQD, it has shown 82.5% accuracy. The existing cases have shown 40.3% accuracy at the stage of designing in which all the RMR factors are considered. It means that at the stage of designing, RMR system can work only with the rock intensity and RQD.

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A proposal of seismic reference velocity ratio for the rock mass classification in tunnel area (터널구간 암반분류를 위한 탄성파 기준속도비의 제안)

  • Ko, Kwang-Beom;Ha, Hee-Sang;Lim, Hae-Ryong
    • 한국지구물리탐사학회:학술대회논문집
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    • 2005.09a
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    • pp.37-42
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    • 2005
  • Remote seismic tomography is regarded as one of the most valuable geophysical technique for the estimation of the rock mass classification in the tunnel area where hard data information such as drill logs are absent. But the results of rock mass classification based on the remote seismic tomography tend to be overestimated in practice. In this study, we propose the effective method to implement the seismic reference velocity ratio based on semblance for the improvement of rock mass classification. Also, to verify its feasibility, proposed technique was tested by using the real field data.

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A Geostatistical Study Using Qualitative Information for Multiple Rock Classification -1. Theory (다분적 암반분류를 위한 정성적 자료의 지구통계학적 연구 1.이론)

  • 유광호
    • Geotechnical Engineering
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    • v.11 no.2
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    • pp.71-78
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    • 1995
  • In this paper, a study was performed on classifying a rock mass into multiple classes as in rock mass classification systems, such as RMR system and Q system etc. In a situation with only limited quantitative data available, it was sought to employ a way of incorporating qualitative data in a systematical and reasonable manner. It is based on the realm of Geostatistics. In particular, indicator kriging technique, which is one of non-parametric approaches, was used. As a selection criterion for an optimal classification, the cost of errors was adopted. As a result, the binary rock classification method developed before was extended and generalized for multiple rock classification with its total number of classes unlimited.

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