• Tunnel and Underground Space
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• v.4 no.3
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• pp.215-227
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• 1994

Rock Mass classifications have been developed in many European countries. The most widely used classification methods are the Rock Mass Rating (RMR) system proposed by Bieniawski(1973) and the Q-system developed By Barton et al. (1974). These methods are also adopted at many mountain tunnels and subway sites in our country. Here, a geomechanical classification for slopeds in rock, the "Slope Mass Rating"(SMR) is presented for the preliminary assessment of slope stabiliyt. This method can be applied to excavation and support design in the front part of tunnel and cutting area as a guide line and recommendation on support methods which allow a systemmetic use of geomechanical classification for rock slopes.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.449-460
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• 2022

Safe underground construction in a rock mass requires adequate ground investigation and effective determination of rock conditions. The estimation of rock mass behavior is difficult, because rock masses are innately anisotropic and heterogeneous at different scales and are affected by various environmental factors. Quantitative rock mass classification systems, such as the Q-system and rock mass rating, are widely used for characterization and engineering design. The measurement of rock classification parameters is subjective and can vary among observers, resulting in questionable accuracy. Geophysical investigation methods, such as seismic surveys, have also been used for ground characterization. Torsional shear wave propagation characteristics in cylindrical rods are equal to that in an infinite media. A probabilistic quantitative relationship between the Q-value and shear wave velocity is thus investigated considering long-wavelength wave propagation in equivalent continuum jointed rock masses. Individual Q-system parameters are correlated with stress-dependent shear wave velocities in jointed rocks using experimental and numerical methods. The relationship between the Q-value and the shear wave velocity is normalized using a defined reference condition. This relationship is further improved using probabilistic analysis to remove unrealistic data and to suggest a range of Q-values for a given wave velocity. The proposed probabilistic Q-value estimation is then compared with field measurements and cross-hole seismic test data to verify its applicability.

• Tunnel and Underground Space
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• v.7 no.1
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• pp.1-12
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• 1997

Engineering rock mass classification is extensively used to determine the reasonable support system throughout the tunneling process in the field. Selection of support system based on the results of engineering rock mass classification is simple and straight-forward. However, this method cannot consider the effect of in-situ stresses, mechanical properties of support material, and support installation time on the behavior or rock-support system To handle the various conditions encountered in the underground excavation sites rock-support system. To handle the various conditions encountered in th eunderground excavation sites rock-support interaction program has been developed. This program can analyze the interaction between rock mass and support materials and also can simulate the tunnel excavation-support insstallation process by controlling the support installation time and the stiffness of support system. Practical applicability of this program was verfied by comparing the results of support design to those from rock mass classification for virtual underground excavation at the drilling site KD-06 in Geoje island.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.265-272
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• 2003

In this study, the rock mass classification results from the face mapping and the resistivity inversion data are compared and analyzed for the reliability investigation of the determination of 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 based on RMR(rock mass rating) are calculated. Kriging method as a post processing technique for global optimization is used to improve its resolution. The result of correlation analysis shows that the geological condition estimated from 2D electrical resistivity survey is coincident globally with the trend of rock type except for a few local areas. The correlation between the results of 3D electrical resistivity survey and the rock mass classification turns out to be very high. It can be concluded that 3D electrical resistivity survey is powerful to set up the reliable rock support type.

• Geomechanics and Engineering
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• v.20 no.2
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Explosives and Blasting
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• v.24 no.1
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• pp.63-69
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• 2006

Building tunnels means dealing with what rock is encountered. Relocation of the site of the underground structure is rarely possible. Tunneling engineers and miners have to cope with the quality of the rock mass as it is. Different tunneling philosophies and different rock classification methods have been developed in various countries. Most of the rock classification methods are based on the response of the rock mass to the excavation. Tunnel support requirements could be assessed analytically, supplemented by rock mass classification predictions, and verified by measurements during construction. Rock mass classifications on their own should only be used for preliminary, planning purposes and not for final tunnel support. Design of blast pattern in tunneling projects in Korea is also mostly prepared according to the general rock classification methods such as RMR or Q. They, however, do not take into account the blast performance, and as a consequence, produce poor blasting results. In this paper, the methods of general rock classification and blast design for tunnel excavation in Korea are reviewed, and efforts to develop a new classification method, reflecting the blasting performance, are presented.

• 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.

• 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.

• 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.