• Tunnel and Underground Space
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• v.21 no.6
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• pp.439-449
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• 2011

The characteristics of rock fractures for engineering rock classification are investigated by analyzing three dimensional point cloud generated from adjusted digital images of a tunnel face during construction and the tunnel is reinforced based on the supporting pattern suggested by the RMR and the Q system using parameters extracted from those images. As results, it is possible saving time required from face mapping to tunnel reinforcing work, enhancing safety during face mapping work in tunnels and reliability of both the mapping information and selecting supporting pattern by storing the files of digital images and related information which can be checked again, if necessary sometime in the future.

• 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.

• Journal of the Korean Geotechnical Society
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• v.35 no.9
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• pp.37-45
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• 2019

Quick identification of the condition of tunnel face and optimized determination of support patterns during tunnel excavation in underground construction projects help engineers prevent tunnel collapse and safely excavate tunnels. This study investigates a CNN technique for quick determination of rock quality classification depending on the condition of tunnel face, and presents the procedure for rock quality classification using a deep learning technique and the improved method for accurate prediction. The VGG16 model developed by tens of thousands prestudied images was used for deep learning, and 1,469 tunnel face images were used to classify the five types of rock quality condition. In this study, the prediction accuracy using this technique was up to 83.9%. It is expected that this technique can be used for an error-minimizing rock quality classification system not depending on experienced professionals in rock quality rating.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Tunnel and Underground Space
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• v.18 no.6
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• pp.427-435
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• 2008

In this paper, review was made for the excavation method and optimum bench length for unstable tunnel face in case of rock classification type V in order to make the best use of in-situ bearing capacity. 3D FEM analyses were performed to investigate the influences on the tunnel face and adjacent area with regard to the pattern and number of bolts when face bolts were used as a supplementary measure. As a result of this study, full section excavation method with sub-bench is effective in reducing the displacement greatly due to early section closure. Displacement-resistant effects in accordance with the bolting patterns are grid type, zig-zag type and then circular type in order of their effect. And horizontal extrusion displacement of tunnel face reduces as the number of bolts increase. A grid type face bolt covering $1.5m^2$ of tunnel face could secure the face stability in case of full section excavation method with sub-bench.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.39-46
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• 2005

The RMR ratings, one by horizontal pre-boring in a tunnel and another by tunnel face mapping, are compared at the fault zone in a tunnel. Generally. the horizontal pre-borings were so effective as to forecast reasonably the supporting patterns after tunnel excavation. But the maximum difference in RMR ratings estimated by two methods was about 50 at a certain section of a tunnel. The differences were analyzed on each parameter of the RMR system: the rating differences were 24 in the condition of discontinuities, 15 in the RQD and 13 in the uniaxial compressive strength of rock. To minimize the gap between RMR by pre-borings and by face mappings, it is necessary to select the horizontal pre-boring location where tunnel stability could be critical and to evaluate in detail the sub-parameters of the condition of discontinuities.

• 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.

• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.463-473
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• 2005

Because of using the same rating ranges for every rock types the RMR or the Q-system could not usually consider local geological characteristics They also could not present sufficiently the engineering anisotropy of rocks. The canonical correlation analysis was carried out with 3 kinds of face mapping data obtained from granite, sedimentary rock and phyllite in order to clarify a discrepancy between rock types. According to analysis results, as a type of rocks changes, RM factors have different influences on the total rating of RMR.

• Tunnel and Underground Space
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• v.21 no.5
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• pp.333-340
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• 2011

RMR is most strongly adopted rock classification method to scheme support system in domestic tunnel. However the RMR, which is based on geological survey during design stage of tunnel, can't present the real ground accurately. In this study, authors suggested Weighted-RMR (W-RMR) which is considered weighted value of risk factors of collapse due to prevent collapse and roof falls during tunneling. According to the application of W-RMR to Bye-Gye tunnel, we could change support type flexibly by the risk factors on a face of tunnel.

• 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.