• Tunnel and Underground Space
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• v.15 no.6 s.59
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• pp.425-431
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• 2005

A series of geotechnical surveys and in-situ tests were carried out to evaluate the stability of underground mine cave in an abandoned coal mine. After the closure of the mine, the underground mine drifts have been utilized for a tourist route since 1999. The dimension of the main cave is 5m width, 3m height and 230m length. The surrounding rock mass of the cave is consist of black shale, coal and limestone. Also, the main cave is intersected by two fault zone. Detailed field investigations including Rock Mass Rating(RMR), Geological Strength Index(GSI) and Q classification were performed to evaluate the stability of the main cave and to examine the necessity of reinforcement. Based on the results of rock mass classification and numerical analysis, suitable support design was recommended for the main cave. RMR and Q values of the rock masses were classified in the range of fair to good. According to the support categories proposed by Grimstad & Barton(1993), these classes fall in the reinforcement category of the Type 3 to Type 1. A Type 3 reinforcement category signifies systematic bolting and no support is necessary for the Type 1 case. From the result of numerical analysis, it was inferred that additional support on the several unstable blocks is required to ensure stability of the cave.

• Tunnel and Underground Space
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• v.28 no.3
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• pp.232-246
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• 2018

In this study, the relationship between in situ seismic wave velocities and RMR (rock mass rating) was investigated in a test bed for the examination of the basis of rock classification (RMR) based on seismic wave velocity. The seismic wave velocity showed a monotonous increase with depth. It was also found that there was no systematic correlation between the seismic wave velocity (Vp) and other parameters (RQD, joint spacing, UCS, rock core Vp, and RMR) collected at the same depth of the same borehole. However, correlative relation was observed among RMR, RQD, and joint spacing. On the other hand, when all the data in the borehole (three holes) are examined without considering the depth, Vp still shows no correlation with RMR parameters (e.g., correlative coefficient for uniaxial compressive strength and joint spacing are 0.039 and 0.091, respectively), but Vp shows weak correlative relation with RMR and RQD (correlative coefficient for RQD and RMR are 0.193 and 0.211, respectively). Thus, it is found that it is difficult to deduce physical properties of rock mass directly from seismic wave velocities, but the seismic wave velocity can be used as a tool to approximate rock mass properties because of weaker correlation between Vp and RMR with RQD. In addition, the velocity value of for soft and moderate rocks suggested by widely used construction standards is slower than that of the observed velocity, implying that the standards need to be examined and revised.

• Tunnel and Underground Space
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• v.9 no.3
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• pp.214-220
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• 1999

In this study, the validity of items of RMR system is evaluated and the applicability of this system to the data measured in Korean sites if discussed. Database was constructed from 139 sites, which are composed of subways, railway tunnels and road tunnels. These sites are located nationwide. Analysis shows that original classification of Bieniawski is valid although it was derived empirically. But it has considerable rating difference (error) in the result of Korean application. Thus new classification systems of KRMRI and KRMR2 are suggested, which are deduced from the Korean database. The former includes adjusted ratings and the latter adopts two more items. These are deduced by artificial neural network because it is difficult to select \`characteristic value'to estimate rock quality.

• 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.

• Land and Housing Review
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• v.3 no.4
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• pp.389-397
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• 2012

In this study, crosshole seismic test, donwhole seismic test, SPT uphole test, and suspension PS logging (SPS logging) were conducted and the shear wave velocities of these tests were compared. The test demonstrated the following result: Downhole tests showed similar results compared to those of crosshole tests, which is known to be relatively accurate. SPS logging showed reliable results in the case of no casing, i.e. in the rock mass, while, in the case of soil ground, its values were lower or higher than those of other tests. SPT-uphole tests showed similar results in the soil ground and upper area of rock mass compared to other methods. However, reliable results could not be obtained from these tests because SPT sampler could not penetrate into the rock mass for the tests.

• Journal of the Korean Geotechnical Society
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• v.17 no.4
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• pp.127-134
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• 2001

본 논문에서는 국내 여러 지역에서 수행된 도로, 철도 및 기타 토목공사를 위한 설계과정에서 조사가 이루어진 현지조사와 시추코아 및 시추공을 대상으로 암반평가가 이루어진 자료들을 대상으로 암반분류방법간의 상관관계에 대해 조사하였다. 상관관계에 대한 해석은 암반분류에서 많이 사용되고 있는 RMR과 Q분류법간의 상관관계 그리고 RQD와 두 암반평가방법간의 관계에 대하여 암석성인별 분류 즉 화성암, 퇴적암 및 변성암별로 검토를 실시하였다. 전체적으로 분류방법의 상관관계는 좋게 나타나고 있다. 그리고 음파검층에 의한 탄성파 P파 속도와 RMR의 상관관계를 고찰하였는데, 이 두 요소간의 상관성은 비교적 양호하였으며 보다 신뢰성 있는 관계식을 유도하기 위한 노력이 필요하다.

• Tunnel and Underground Space
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• v.8 no.4
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• pp.296-306
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• 1998

Ongoing huge Bakun Hydropower project is including the construction of a 210 m height hydroelectric rockfill dam with an installed capacity of 2,520 MW and a power transmission system connecting to the existing networks between Sarawak and peninsula Malaysia. In order to allow the main dam construction during the dry season, the Ballui river will have to be detoured through 3 concrete lined diversion tunnels with an internal diameter of 12 m and a length of 1,400 m each. The geology of Bakun site belongs to the several thousand meters thick Belaga formation deposited from the late Cteteceous to the early Teriary in the Northwest Borneo geosyncline. The orientation of the bedding plane, strike at N55$^{\circ}$E to N70$^{\circ}$E and dip at 50$^{\circ}$SE to 70$^{\circ}$SE, is developed uniformly in Bakun sedimentary rocks. Rock mechanical characteristics of Bakun site have been classified into 4 rock mass types(RMT) depending on the degree of weathering and the occurrence of rock jointing with RMR. Graywacke(Sandstone) as well as Shale can take place together in the same rock mass type if their rock mass properties are similar. It was summarized the rock support type and support system design of underground diversion tunnels in view of rock mechanics.

• Journal of the Korean Geophysical Society
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• v.7 no.4
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• pp.293-304
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• 2004

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.239-248
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• 2020

Exact rock classification helps suitable support patterns to be installed. Face mapping is usually conducted to classify the rock mass using RMR (Rock Mass Ration) or Q values. There have been several attempts to predict the grade of rock mass using mechanical data of jumbo drills or probe drills and photographs of excavation surfaces by using deep learning. However, they took long time, or had a limitation that it is impossible to grasp the rock grade in ahead of the tunnel surface. In this study, a method to predict the Q value ahead of excavation surface is developed using recurrent neural network (RNN) technique and it is compared with the Q values from face mapping for verification. Among Q values from over 4,600 tunnel faces, 70% of data was used for learning, and the rests were used for verification. Repeated learnings were performed in different number of learning and number of previous excavation surfaces utilized for learning. The coincidence between the predicted and actual Q values was compared with the root mean square error (RMSE). RMSE value from 600 times repeated learning with 2 prior excavation faces gives a lowest values. The results from this study can vary with the input data sets, the results can help to understand how the past ground conditions affect the future ground conditions and to predict the Q value ahead of the tunnel excavation face.

• Tunnel and Underground Space
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• v.3 no.2
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• pp.167-174
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• 1993

Index tests are useful because they are rapid and cheap-and if bias is known the fundamental property can be estimated, as when estimating the compressive strength or the tensile sterngth from the rock test hammer value. Index tests which have proved to be very useful are the rock test hammer, the point load test and sonic velocity test. These can provide index values in their owing right or be used to estimate the compressive strength, the tensile strength and elastic modulus. Because of the heterogeneous and fractured nature of rock, many index tests have been developed for a variety of purposes, e.g.for use in rock mass classification schemes.