• 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.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.03a
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• pp.69-78
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• 2008

• Geotechnical Engineering
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• v.9 no.3
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• pp.61-66
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• 1993

In this paper, the incorporation of qualitative(or soft) data, such as outputs of geophysical tests or construction experience which has so far been cumulated, was discussed for rock classsification. Geostatistics wart used for this research since the parameters for the design of tunnels are spatially correlated. In particular, indicator kriging technique, which is one of non -parametric approaches, was used. As a selection criteria for an optimal classification, the cost of errors was adopted and the binary classes were only considered for rock classification. In future, incorporating an appreciable amount of available qualitative data will be necessary in tunnelling projects in which quantitative data are scarce. In this respect, this research is of great significance.

• Tunnel and Underground Space
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• v.28 no.4
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• pp.358-371
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• 2018

The stability of openings in the underground mine is major concern in the operation of mines that must ensure productivity and safety. Among many rock conditions affecting cavities stability, the width and height of the opening is an important design factor. In this paper, we consider to determine the maximum unsupported span of a opening in a limestone mine by using the Q system among several rock classification schemes. In order to determine the span of the unsupported opening in the limestone mine, rock mass classifications were carried out at over 200 sites in the underground limestone mines. The relationships by using the Q system and the stability graph proposed by Mathews to determine the maximum span of the unsupported opening were derived and compared. We propose a new classification method that combines GSI and RMR rock classification systems to make it easy to use in a field.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Tunnel and Underground Space
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• v.25 no.2
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• pp.210-219
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• 2015

This paper describes a new quantitative process for evaluating the roughness of discontinuity, which is suggested as a qualitative criteria in RMR or Q-system. For this purpose, the Delphi method which is one of the surveying methods was introduced. The selected panels were asked to evaluate the roughness of discontinuities on the Web which was hosted by authors in advance. A total of 3 surveys were performed using JRCs suggested by Barton and Choubey as well as Ai generated by the Monte Carlo simulations. After each survey, the results were provided to all panels for comparing their decisions to others. As surveys proceeded, better consensus and convergence were achieved. With a good agreement of panels on roughness classification, the quantitative criteria for roughness of discontinuity in RMR and Q-system was established in this study.

• 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.29 no.5
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• pp.305-317
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• 2019

The RMR and Q-system for characterizing rock mass and drilling core, and for estimating the support and reinforcement measures in mine galleries, tunnels and caverns have been widely used by engineers. SMR has been widely used in the rock mass classification for rock slope, but Q-Slope has been introduced into slopes since 2015. In the last ten years, a modified Q-system called Q-slope has been tested by the many authors for application to the benches in open pit mines and excavated road rock slopes. The results have shown that a simple correlation exists between Q-slope values and the long-term stable and unsupported slope angles. Just as RMR and Q have been used together in a tunnel or underground space and complemented by comparison, Q-Slope can be used in parallel with SMR. This paper introduces how to use Q-Slope which has not been announced in Korea and application examples of Pasir open pit coal mine in Indonesia.

• Tunnel and Underground Space
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• v.11 no.4
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• pp.311-318
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• 2001

RQD is used to evaluate the degree of fracture in the rock mass and is also used as input into rock mass classification scheme, such as RMR and Q-system. However there are some drawbacks of the RQD caused by anisotropy and calculation length. Thus it is important to understand the variation of RQD in 3-D space in order to evaluate the properties of rock mass. The main purpose of this study is to reveal the distribution of RQD in the equal-angle stereo net, to investigate the effects of scanline length and joint frequency and to inquire the effect on the selection of rock mass strength parameters in the numerical analysis. Analysis has been extended to field joint survey data using same method. The results can be applied to contribute for more accurate interpretation of the results of engineering geological survey for better design and construction work.