• The Journal of Engineering Geology
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• v.16 no.3 s.49
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• pp.283-291
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• 2006

Shotcrete is used as a permanent lining in single shell tunnels even though shotcrete has been used as a temporary lining in NATM tunnels. Therefore, the accurate evaluation of strength parameters is very crucial because the reliable estimation of loads acting on the shotcretes is necessary to maintain the stability of tunnels. The evaluation of strength parameters of the ground far the single shell tunnels should be investigated to adapt the method in Korea because the geological condition of Korea is different from that of other country. Rock classification and strength parameters obtained from 25 tunnel sites were investigated for this study. Support types fur the different rock classes are suggested for the single shell tunnels in Korea based on the NMT because Q-system has been widely used in Korea. The support types in terms of both Q and RMR values are given based on the correlation of Q and RMR values obtained from the case studies.

• Tunnel and Underground Space
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• v.13 no.1
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• pp.6-19
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• 2003

This study has for its object to increase an objectivity of the observation result in the face mapping of tunnel and to suggest the reasonable support and reinforcement methods to be considered the rock properties. It was developed in this study to the tunnel stability evaluation system(Prototype NFEST) to be used fuzzy set theory and neuro-fuzzy techniques, and this system was verified according to the reliability evaluation between the 36 learning data and the inferred results. When it summarized the results; (1) 12 evaluation items and ranges were proposed to be modified basis on the RMR which are well known to the domestic workers. (2) It was shown that correlation coefficient(│R│) between $RMR_{inf}$ inferred by 12 items and $RMR_{org}$ due to arithmetic total, $RMR_{chk}$ due to subjective judgement of observer are relatively high relationship with each 0.83 and 0.79. (3) Inferred result of the total tunnel safety shows also a good relationship with $RMR_{inf}$ (│R│=0.7) and the rock weathering(│R│=0.84).

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.13-23
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• 2007

Weathered soils appear from the rock and its weathering result. In accordance with the degree of weathering the roch may become soft rock, weathered rock and residual soil. In general, classification method for determining the degree of weathering are performed by chemical method and N-value. But these method have some problems. So, this research is to suggest an appropriate physical method to determine the degree of weathering of weathered soils. A new classification method for determining the degree of weathering is suggested, based upon the results from fall cone test. According to the proposed physical method using fall cone apparatus, the measured values of the samples from the same area show distinctive difference of weathering. For the checking, we selected two areas. As a result, the relationship between CWI and water content according to penetration is expressed as an equation in Ilsan and Incheon area. And it proved to be a good method to determine the degree of weathering.

• Explosives and Blasting
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• v.22 no.2
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• pp.33-43
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• 2004

The six grades weathering system is normally used in weathered rock classification. In this study. fresh and weathered rock block of grade I to V were sampled in Jang-soo ana but samples of the grade VI was omitted from this study. The variation quantities of chemical weathering indices with weathering degree are smaller than those of physical and mechanical properties. Increase of Weathering degree is well indicated by physical and mechanical properties such as strength, hardness, ultrasonic velocity and slake durability result. Especially, absorption and porosity ratio is a good indicator. As weathering proceeds. a number of the cracks affect the rock deformation. Therefore, stress-strain curves of weathered rocks in unconfined state are quite different from ones of fresh rocks.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• The Journal of Engineering Geology
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• v.17 no.4
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• pp.555-566
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• 2007

In general, RMR classification system is used for the support design of a tunnel. Face mapping during excavation and RMR-based rock classifications are conducted in order to provide information for complementary changes to preliminary survey plans and for continuous geological estimations in direction of tunnel route. Although they are ever so important, there are not enough time for survey in general and sometimes even face mapping is not available. Linear regression analysis for the estimation of mediating RQD and condition of discontinuities, which require longer time and more detailed observation in RMR, was performed and optimum regression equations are suggest as the result. The geological data collected from tunnels were analyzed in accordance with three rock types as sedimentary rock, phyllite and granite to see geological effects, generally not been considered in previous researches. Parameters for the regression analysis were set another RMR factor.

• The Journal of Engineering Geology
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• v.5 no.2
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• pp.167-179
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• 1995

The only three elements such as RQD, N -value and Es were used as a quantitative standard for the design of supporr pattern determidetion on subway line 8th in Seoul. Because the support pattern that was obtained by these elements could not he determined on the basis of the quantitative of geology and the orientations and properties of discontinuity planes, there have been some problems in determining the economic support pattern and tunnel stability. Therefore, in an attempt to determine the stable and economic support pattern with more quantitative elements, more flerrible rock mass classification with geologic conditions was performed by using RMR at 1745 sections and Q-system at 374 sections within Seongnam block on subway line 8th. Then, rusults by these two methods were compared with standard support pattern of the subway line 8th. Moreover, relationships between geology, geologic structures and topography to rock mass grades were studied. According to the rusult of this study, it is judged that the standard support pattern designed with PD-4 or PS - 4 should have been subdivided into 4~6 support patterns. Some sections where geologic structures such as faults and joints are developed tend to have rock mass grades. And they also have low rock mass grades near valley. On thr other hand, they show intermediate grades at piedmont area and the greatest ones at high mountains.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.350-360
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• 2008

Electrical resistivity is one of physical property of the earth and measured by electrical resistivity survey, electrical resistivity logging and laboratory test. Recently, electrical resistivity is widely used in determination of rock quality in support pattern design of road and railway tunnel construction sites. To get more reliable rock quality data from electrical resistivity, it needs a lot of test and study on correlation of resistivity and rock quality. Firstly, we did rock property test in laboratory, such as P wave velocity, Young's modulus, uniaxial compressive strength (UCS) and electrical resistivity. We correlate each test results and we found out that electrical resistivity has highly related to P wave velocity, Young's modulus and UCS. Next, we accomplished electrical resistivity survey in field site and carried out electrical resistivity logging at in-situ area. We also performed rock classification, such as RQD, RMR and Q-system and we correlate electrical resistivity to RMR data. We found out that electrical resistivity logging data are highly correlate to RMR. Also we found out that electrical resistivity survey data are lower than electrical resistivity logging data when there are faults or fractures. And it cause electrical resistivity survey data to lowly correlate to RMR.

• Geomechanics and Engineering
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• v.35 no.5
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• pp.539-554
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• 2023

This article is dedicated to the pursuit of establishing a robust empirical relationship that allows for the estimation of in-situ modulus of deformations (E_m and G_m) within sedimentary rock slope masses through the utilization of Q_slope values. To achieve this significant objective, an expansive and thorough methodology is employed, encompassing a comprehensive field survey, meticulous sample collection, and rigorous laboratory testing. The study sources a total of 26 specimens from five distinct locations within the South Pars (known as Assalouyeh) region, ensuring a representative dataset for robust correlations. The results of this extensive analysis reveal compelling empirical connections between E_m, geomechanical characteristics of the rock mass, and the calculated Q_slope values. Specifically, these relationships are expressed as follows: E_m = 2.859 Q_slope + 4.628 (R² = 0.554), and G_m = 1.856 Q_slope + 3.008 (R² = 0.524). Moreover, the study unravels intriguing insights into the interplay between in-situ deformation moduli and the widely utilized Rock Mass Rating (RMR) computations, leading to the formulation of equations that facilitate predictions: RMR = 18.12 E_m0.460 (R² = 0.798) and RMR = 22.09 G_m0.460 (R² = 0.766). Beyond these correlations, the study delves into the intricate relationship between RMR and Rock Quality Designation (RQD) with Qslope values. The findings elucidate the following relationships: RMR = 34.05e^0.33Qslope (R² = 0.712) and RQD = 31.42e^0.549Qslope (R² = 0.902). Furthermore, leveraging the insights garnered from this comprehensive analysis, the study offers an empirically derived support system tailored to the distinct characteristics of discontinuous rock slopes, grounded firmly within the framework of the Q_slope methodology. This holistic approach contributes significantly to advancing the understanding of sedimentary rock slope stability and provides valuable tools for informed engineering decisions.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.