• Tunnel and Underground Space
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• v.21 no.5
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• pp.359-369
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• 2011

In this study, physical, mechanical, and thermal properties of rock samples were investigated to estimate the Excavation Damaged Zone (EDZ) developed during the construction of the KAERI Underground Research Tunnel. The average porosity in the EDZ was increased by about 140%. The average wave velocity, Young's modulus, and uniaxial compressive strength in the EDZ were decreased by about 11, 37, and 16%, respectively. And the thermal conductivity in the EDZ was decreased by about 20%. From the laboratory tests, the EDZ size could be estimated to be around 1.1-2.4 m.

• Tunnel and Underground Space
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• v.7 no.4
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• pp.274-283
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• 1997

It is necessary to estimate the soundness of tunnel using non-destructive tests(NDT) for effective repairs and maintenances. But, the state of tunnel lining could not be investigated using previous non-destructive techniques, due to the various types of support and accessibility only from one side in tunnel lining. Recently, the various non-destructive techniques such as ground penetrating radar(GPR) have been researched and developed for inspection of tunnel lining. In this study, the usefulness and applicability of GPR test in tunnel lining inspection has been investigated through model tests and tunnel site application. This paper described the tunnel lining inspection for lining thickness, cavity and support using GPR test. From the results of tests, we have concluded that GPR test are very useful and effective techniques to look into the interior of lining and measure the lining thickness.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• Journal of the Korean Society for Railway
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• v.12 no.1
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• pp.31-38
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• 2009

The selection of the support system is an important design parameter in design and construction of the tunnel using the new Australian tunnel method. It is a common practice to select the support based on the rock mass grade, in which the rock mass is classified into five rock groups. The method is applicable if the characteristics of the rock mass are uniform in the direction of tunnel excavation. However, such case is seldom encountered in practice and not applicable when the properties vary along the longitudinal direction. This study performs comprehensive three dimensional finite difference analyses to investigate the ground deformation pattern for cases in which the rock mass properties change in the direction of the tunnel axis. The numerically calculated displacements at the tunnel crown show that the displacement is highly dependent on the stiffness contrast of the rock masses. The results strongly indicate the need to select the support type $0.5{\sim}1.0D$ before the rock mass boundary. The paper proposes a new guideline for selecting the support type based the results of the analyses.

• Journal of the Korean GEO-environmental Society
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• v.3 no.3
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• pp.5-13
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• 2002

In tunnel design, geotechnical survey of over 200m tunnel depth is required because of its characteristical topography. For this reason, there are difficulties in collecting information of basic data in tunnel design because of large-scale costs in borehole tests, of limits to a geotechnical analysis by the existing refraction seismic survey and of analytical errors in steep mountainous area. Seismic tomography has many advantages as follows; 1) seismic velocity as absolute value is more reliable than electrical resistivity, 2) geotechnical analysis in deep tunnel depth is available by seismic velocity, 3) analytical errors is reduced in steep mountainous area. In this paper, it was found out a correlation of seismic velocity and Q in tunnel design in the neighborhood of the National Capital region and the reduction effect of tunnel construction cost using reliable rock quality by seismic tomography compared with by borehole data and electricity resistivity data.

• Tunnel and Underground Space
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• v.21 no.4
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• pp.251-263
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• 2011

The geomechanical characteristics of rock and the structural geological feature of the fault should be studied and examined for the successful construction of large-span tunnel. In this case study, that is a important case for the tunnel collapse and reinforcement during the construction for the waterway tunnel at large thrust fault zone in schist, we carried out geological and geotechnical survey for make the cause and mechanism of tunnel collapse. Also, we have designed the reinforcement and re-excavation for the safe construction for collapse zone and have carried out successfully the re-excavation and finished the final concrete lining.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.229-236
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• 2002

In this paper, a new novel technique of seismic survey is introduced to estimate the continuity of inclined pits filled with water, It was assumed that the pits would be connected to an abandoned railway tunnel that might be constructed in the past. Thus, detection of pit end was needed for the design of a new highway tunnel(Yukshimreong tunnel) that was likely to be met with a pit. In the beginning of exploration, no reliable, cost effective method was available. Hence, focus of interest moved toward the high impedance contrast(reflection coefficient k∼0.8) between water and rock. In this special model of sequence rock-water-rock, total reflection occurs and the seismic energy, when it is generated in the pit water, is nearly confined to the pit so that seismic waves can propagate much further within the pit. As a matter of convenience, this is called“tunnel channel wave”. With these considerations in mind, seismic detonator(2g) was used as a source at the entrance of pit, whereas hydrophone chain(hydrophone interval=1m) was placed on the bottom of pit. With this appropriate source-receiver arrangement, desirable down-going and up-going waves could be observed that will help conform the continuity of pits. After about one year, it was ascertained that the inclined pit of interest was just nearby crossed with the newly excavated tunnel, as it was predicted.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.43-51
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• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.3-16
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• 2008

The underground construction and mining are facing many geomechanics challenges stemming from, geological complexities and stress-driven rock mass degradation processes. Brittle failing rock at depth poses unique problems as stress-driven failure processes often dominate the tunnel behaviour. Such failure processes can lead to shallow unravelling or strainbursting modes of instability that cause difficult conditions for tunnel contractors. This keynote address focuses on the challenge of anticipating the actual behaviour of brittle rocks in laboratory testing, for empirical rock mass strength estimation, and by back-analysis of field observations. This paper summarizes lessons learned during the construction of deep Alpine tunnels and highlights implications that are of practical importance with respect to constructability. It builds on a recent presentation made at the $1^{st}$ Southern Hemisphere International Rock Mechanics Symposium held in Perth, Australia, in September this year, and includes results from recent developments.