• Tunnel and Underground Space
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• v.6 no.2
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• pp.131-143
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• 1996

In-situ survey and laboratory rock test were carried out for rating rock mass around the tunnel that some failures had been occurred in Danyang limestone quarry. For rating rock mass, several methods such as RMR, Q-system, rock strength etc. were applied. The stability analysis on tunnel was evaluated by numerical method FLAC. And The block theory using streographic projection was also applied for stability analysis. The 3-4 major discontinuity sets are distributed in rock mass around tunnel.

• 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.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.209-216
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• 1994

Rock mass classifications have played an indispensable role in underground construction for several decades. An important issue in rock mass classifications is the selection of the parameters of greatest significance. There appears to be no single parameter that can fully describe a jointed rock mass for underground construction design. In this paper. We find some problems shen applied rock mass classification for underground construction in domestic, analyze the most significant parameters and parameters correlation influencing the behavior of a rock mass, and suggest the Simplied Rock Mass Rating system based on RMR method for effective underground supports.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.151-162
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• 2009

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

• 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.

• Tunnel and Underground Space
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• v.16 no.3 s.62
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• pp.203-208
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• 2006

This report is to introduce an article to compare 3 kinds of methods as RMR, Q-system and RMi published in Tunnel and Tunnelling Technology 2003. As rock mass classification is applied to estimate the amount of the support as an empirical design method, an attempt has been made to evaluate the parameters for classifications and their variations by Professor Nilsen and his team in Norway. Representability and reproducibility in measuring the field parameters are discussed and sensitivity of rating values in the three methods is also analyzed in this research. Although some parameters have high variation in rating among the 5 observers, the rock mass class has been found to be quite similar.

• Tunnel and Underground Space
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• v.13 no.5
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• pp.353-361
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• 2003

For RMR and Q rock mass classification at the design and construction stage, evaluation of groundwater condition is usually based upon the experience due to the restriction of available methods. Based on the results of Taejon LNG Pilot Cavern which acquire joint water pressure, inflow rate of ground water and hydraulic conductivity model, estimates from numerical analysis and analytical solutions were compared to verify each evaluation method. As the result, the Raymer(2001) approach was found to be efficient for estimating inflow rate and corresponding value.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.5 no.5
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• pp.22-29
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• 2002

This study was conducted to suggest develop revegetation methods and to classification of cutting-rock slopes revegetation type. The data was collected from pre-experienced data, reports and journal. Also research result was reflected from field research for the conditions of construction, vegetation types and field conditions. As the result of analyze, the factors affecting the plant coverage rates of cutting-rock slopes were period of construction, revegetation methods, slope gradient and slope length. Classification of cutting-rock slopes revegetation type was fourth from material of revegetation measures and spray type. It is recommended to adjust the proposed factor as environment, field condition and characteristic related with revegetation measures on slopes for the presentation of revegetation standard.

• Tunnel and Underground Space
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• v.15 no.6 s.59
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• pp.441-451
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• 2005

It is the unavoidable problem that the RMR rock classification method has the uncertainty resulted from uncertain definition of measured value in RMR grade table, hence in this paper, the estimation of probability density function$(p{\cdot}d{\cdot}f)$ graph with the evaluation of continuos RMR and the Monte Carlo Simulation and statistic reasoning were carried out to evaluate the uncertainty quantitatively. Also, the modified RMR rock classification table was presented in order to apply the uncertainty of RMR to the practice, and then the design process of standard support pattern and the tunnel support material was proposed.

• Tunnel and Underground Space
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• v.14 no.2
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• pp.86-96
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• 2004

Rock mass classification methods such as RMR, Q system and GSl have been widely adopted with certain modifications for the design of mine openings. The GSI system is the only rock mass classification system that is related to Mohr-Coulomb and Hoek-Brown strength parameters and gives a simple method to calculate the engineering properties of rock masses which can be useful input parameters for a numerical analysis. A detailed surveying for GSI mapping as well as far calculating RMR values was undertaken at Daesung and Pyunghae underground limestone mining sites. RQD values were determined for row locations in these two mining sites. Based on GSI values and intact rock strength properties, the rock mass strength modulus of elasticity as well as the Mohr-Coulomb strength parameter c$_{m}$ and $\phi$$_{m}$ were determined. GSI and RMR are correlated.