• Tunnel and Underground Space
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• v.19 no.6
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• pp.479-489
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• 2009

Appropriate investigation of ground condition near excavation face in tunnelling is an inevitable process for safe and economical construction. In this study mechanical parameters from drilling process for blasting were investigated for the purpose of predicting the ground condition, especially rock mass strength, ahead of tunnel face. Rock mass strength is one of the most important factors for classification of rock mass and making a decision of support type in underground construction. Several rock specimens which are considered homogeneous and having different strength values respectively were tested by hydraulic drill machines generally used. As a result, penetration rate is fairly related with rock mass strength among drilling parameters. It is also found that penetration rate increases along with the higher impact pressure even under same rock strength condition. It is finally suggested that new prediction method for rock mass strength using percussive pressure and penetration rate during drilling work can be utilized well in construction site.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.06b
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• pp.51-64
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• 2003

Bieniawski에 의해 개발된 RMR은 암석강도 및 불연속면, 지하수 등의 6개 인자에 따라 분류되어, 이들을 합산하여 결정된다. RMR 분류법은 각 요소들에 대한 평가가 비교적 쉽고, 다양한 응용을 거쳐 여러 분야에 적용되어 국내에서도 가장 널리 사용하고 있는 암반분류 방법 중의 하나이다. RMR 분류결과는 터널의 유지시간, 최대 무지보폭의 예측, 지보량 산정, 암반의 물리적 특성값 예측 등에 적용될 수 있다. 또한 RMR 분류법을 사면안정, 댐 기초, 심부 광산 등에 적용하거나, RMR 분류법의 미비한 부분을 보완하기 위한 여러 가지 수정방법이 제시되었다.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.10a
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• pp.239-246
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• 2005

slaking 은 굴착에 의해 노출된 암반에서 발생하는 강도저하 및 입자간의 결합력 약화에 의해 암반이 세립화하는 현상이다. 이러한 slaking은 특히 퇴적암으로 구성된 암반사변의 안정성에 영향을 미치는 중요한 인자로 작용한다. slaking에 의한 암반사면의 불안정성은 신생대의 이질암이나 미고결 응회암에서와 같이 암반 자체의 강도 저하 및 결합력 약화에 의해 발생하는 붕괴현상과 차별풍화에 의해 이암 등이 급속도로 쇄굴 및 풍화되어 상부에 놓여 있는 암석이 낙석 등의 형태로 붕괴되는 현상으로 구분할 수 있다. 본 연구에서는 이암의 차별풍화에 의해 사면의 불안정성이 유발되는 연구지역을 대상으로 풍화 및 쇄굴 속도와 slake의 상관관계를 밝히고자하였다. 이를 위하여 slake test와 slake durability test를 수행하였으며 slake durability index를 획득하였다. 실험을 통해 획득된 slake durability index를 연간 쇄굴속도와 비교하여 상관관계를 검토하였으며 기존의 연구결과와 비교하여 slake durability index를 활용하여 쇄굴 정도를 예측할 수 있는 가능성을 제시하였다.

• Tunnel and Underground Space
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• v.16 no.3 s.62
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• pp.251-258
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• 2006

In this paper, the applicability to the Korean rock condition of using the deformation moduli based on Rock Mass Rating (RMR) and Pressuremeter Test (PMT) is evaluated. The correlations among deformation moduli and various rock properties were also analyzed. It appears that the existing correlations using RMR overestimate the deformation moduli and wide variation was found between predicted moduli using these correlations and measured values. As for the correlations among the deformation moduli and various rock properties, Rock Quality Designation (RQD) and unconfined compressive strength (UCS) were found to correlate to deformation moduli reasonably well, but joint spacing and joint conditions appear to correlate poorly to RQD and UCS. Additionally, groundwater can not be correlated with the modulus values. While the depth has very little contribution to deformation modulus, it should be factored in the simple regression analyses with various rock mass properties, especially with the correlations made with UCS, RQD etc. With the deficiencies of these correlations, more in depth analysis techniques such as multivariate correlations may be to reliably estimate deformation modulus of rock mass.

• Tunnel and Underground Space
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• v.33 no.3
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• pp.141-149
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• 2023

Recently, as the demand for urban underground space increases, urban tunnel planning is actively progressing. In particular, the application of the roadheader excavation method, which has favorable applicability to urban tunnel, is increasing. However, it is known that the roadheader excavation method has a limitation in that excavation efficiency for high strength rock with a Uniaxial Compressive Strength (UCS) of 100 MPa or more is lowered. In this study, The pre-cracked method was presented as a method to improve the excavation efficiency of roadheader for high strength rock and its applicability was evaluated. The net cutting rate was evaluated using the Bilgin prediction formula, which can calculate the net cutting rate by considering the UCS and RQD (Rock Quality Designation). It was found that the net cutting rate increased as the RQD decreased under the rock condition with the same UCS. This is judged to increase the excavation efficiency of the roadheader in the jointed high strength rock. Additionally, the field applicability of the pre-cracked method for high strength rock was verified through field tests. It was confirmed that the crack zone was formed around the charging hole, and it is considered that the pre-cracked method can be applied to the high strength rock.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• Journal of the Korean Geotechnical Society
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• v.29 no.11
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• pp.5-15
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• 2013

The end bearing capacity of rock-socketed drilled shafts under axial loading is investigated by Hoek-cell tests and a numerical analysis. From the test results, it was found that the ultimate end bearing capacity ($q_{max}$) was influenced by pile diameter, rock mass modulus and the spacing of discontinuity. A new ultimate end bearing capacity method is proposed by taking end bearing capacity influence factors, including rock mass discontinuity, based on field data. Through comparisons with other field data, the proposed $q_{max}$ method represents a definite improvement in the prediction of ultimate end bearing capacity of rock-socketed drilled shafts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1C
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• pp.25-31
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• 2006

It is common to use the unconfined compressive strength (UCS) of intact rock to estimate the shaft resistance of rock socketed drilled shaft. Therefore the most design manuals give a guide to use the UCS of rock core to estimate the shaft resistance of rock-socketed drilled shaft. Recently, however the design manuals for highway bridge (KSCE, 2001) and of AASHTO (2000) were revised to use the UCS of rock mass with RQD instead of the UCS of rock core so that the estimated resistance could be representative of field conditions. Questions have been raised in application of the new guide to the domestic main bed rock types. The intrinsic drawbacks in terms of RQD were comprised in the questions, too. As the results, in 2002 the new guide in the design manual for highway bridge (KSCE, 2001) were again revised to use the UCS of rock core to estimate the shaft resistance of rock-socketed drilled shafts. In this paper, various methods which can estimate the UCS of rock mass from intact rock core were reviewed. It seems that among those, the Hoek-Brown method is very reliable and practical for the estimation of the UCS of rock mass from rock cores. As the results, using the Hoek-Brown failure criterion a modified guide for the estimation of the shaft resistance of rock-socketed drilled shafts was suggested in this paper. Through a case study it is shown that the suggested method gives a good agreement with the measured data.

• Tunnel and Underground Space
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• v.14 no.5
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• pp.353-362
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• 2004

Prediction of fragmentation in bench blasting is one of the most important factors to establish the production plan. It is widely accepted that fragmentation could be accurately predicted using the Kuz-Ram model in bench blasting. Nevertheless, the model has an ambiguous or subjective aspect in evaluating the model parameters such as joint condition, rock strength, density, burden, explosive strength and spacing. This study proposes a new method to evaluate the parameters of Kuz-Ram model, and the predicted mean fragment sizes using the proposed method are examined by comparing the measured sizes in the field. The results show that the predictions using Kuz-Ram model with the proposed method coincide with field measurements, but Kuz-Ram model does not reflect the in-situ rock condition and hence needs to be improved.

• 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.