• 터널과지하공간
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• 제21권5호
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• pp.359-369
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• 2011

본 연구에서는 굴착 전 후에 채취한 암석시료들에 대해 물리적, 역학적 그리고 열적 물성을 조사하여, KAERI Underground Research Tunnel(KURT)의 건설로 인해 발생된 굴착손상영역(EDZ)을 정량적으로 평가하고자 하였다. 굴착손상영역에서 공극률은 약 140% 정도 증가하였고, 탄성파속도, 탄성계수, 그리고 일축압축강도는 각각 약 11, 37, 그리고 16% 정도 감소하였다. 또한 굴착손상영역에서의 열전도도는 약 20% 정도 감소하였다. 암석물성변화를 이용하여 KURT 굴착손상영역의 범위를 판단한 결과 약 1.1-2.4 m로 나타났다.

• 터널과지하공간
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• 제7권4호
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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.

• 터널과지하공간
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• 제3권1호
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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.

• 한국철도학회논문집
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• 제12권1호
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• pp.31-38
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• 2009

국내에서 터널 설계시 암반의 물리적, 역학적 특성에 따라서 $5{\sim}6$개의 암반등급으로 분류한 후 터널의 용도 및 특성을 고려하여 지보시스템을 결정하게 된다. 그러나 이와 같은 방법은 암반의 특성이 균일하다는 가정을 하고 있으며 암반특성이 종 방향으로 변화될 경우 이에 대한 지보시스템의 친정이 달라져야 한다. 본 연구는 3차원 수치해석 프로그램(FLAC3D)을 이용하여 NATM 터널시공시 암반특성의 종방향 변화가 암반분류 및 지보시스템 결정에 미치는 영향을 파악하고자 총 14Case를 현장의 시공순서를 고려한 해석을 수행하였다. 암반특성의 종방향 변화시 전 후방 암반의 강성차이가 작은 경우에는 암반경계를 기준으로 0.5D내외, 강성차이가 큰 경우에는 1.0D 내외의 범위에서 유리한 암반등급의 거동과는 다르므로 암반특성에 따라서 암반경계층을 기준으로 $0.5D{\sim}1.0D$구간을 안전측(보수적)으로 평가하여 설계에 반영하거나, 지보패턴을 하향조정하는 것이 시공성, 작업효율성, 공사기간 등의 측면에서 효과적일 것으로 판단된다.

• 한국지반환경공학회 논문집
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• 제3권3호
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• pp.5-13
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• 2002

본 연구에서는 수도권 근처의 터널설계시 대심도 탄성파 토모그래피 탐사에 의한 탄성파 속도와 Q 값과의 상관관계를 도출하고 이를 통한 정확한 암반분류로 공사비 절감효과에 대하여 고찰하였다. 터널전구간 지표면에서 탐사를 수행하여 대심도 구간의 자료를 얻어 암반분류를 수행하였으며, 이상대 구간에서는 VSP를 통한 자료를 추가하여 보다 상세한 자료를 얻어 신뢰성 향상을 얻을 수 있었다.

• 터널과지하공간
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• 제21권4호
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• pp.251-263
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• 2011

대단면 터널의 안전한 시공을 달성하기 위해서는 굴착암반의 공학적 특성 및 단층 등과 같은 구조지질적인 특성에 대한 파악이 무엇보다 중요하다. 본 현장은 운모 편암내에 대규모 스러스트 단층이 존재하고 있어, 대단면 터널 굴착시 터널의 안정성에 매우 심각한 영향을 미치는 지질공학적 리스크가 특히 큰 현장이라고 할 수 있다. 본 연구에서는 스러스트 단층의 구조지질적 분포 특성 및 단층암의 공학적 특성에 대한 지반조사결과를 바탕으로 터널낙반의 원인과 메카니즘을 분석하고, 이를 바탕으로 대단면 터널의 안전한 재시공을 위한 합리적인 보강대책을 수립하였다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 봄 학술발표회 논문집
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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.

• 한국암반공학회:학술대회논문집
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• 한국암반공학회 2008년도 국제학술회의
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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.

• 한국암반공학회:학술대회논문집
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• 한국암반공학회 2008년도 국제학술회의
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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.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 추계학술대회 논문집
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• pp.1089-1094
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• 2004

Numerical methods to estimate behaviors of jointed rock mass can be roughly divided into two method : discontinuous model and continuum model. Generally, distinct element method (DEM) is applied in discontinuous model, and finite element method (FEM) or finite difference method (FDM) is utilized in continuum model. To predict a behavior of discontinuous model by DEM, it is essential to understand characteristics of joints developed in rock mass through field tests. However, results of field tests can not provide full information about rock mass because field tests is conducted in limited area. In this paper, discontinuous analysis by UDEC and continuous analysis by FLAC is utilized to estimate a behavior of a tunnel in jointed rock mass. For including discontinuous analysis in continuous analysis, joints in rock mass is considered by reducing rock mass properties obtained by RMR and decreasing shear strength of rock mass. By comparing and revising two analysis results, analysis results similar with practical behavior of a tunnel can be induced and appropriate support system is decided.