• 기술사
• /
• 제24권5호
• /
• pp.77-80
• /
• 1991

• 터널과지하공간
• /
• 제13권3호
• /
• pp.196-206
• /
• 2003

본 연구에서는 Raise Boring Machine(RBM의 가동율, 관입율, 굴진율과 같은 굴착능력을 조사하기 위하여 직경 3.05 m와 총 연장 98 m의 수직구를 RBM을 이용하여 시험시공 하였다. 이와 함께 국내 양수발전소, 도로터널, 석탄광업소 등에서 RBM으로 시공되었던 4개의 수직구 시공현장으로부터 시공자료를 수집하여 분석을 수행하였다. 연구결과, 주간 평균 굴진장은 약 19.3 m로 분석되었고, 평균 가동율은 약 54.3%011서 75.1 %사이에 분포하는 것으로 나타나, 이는 TBM 시공실적과 비교하여 볼 때 매우 높은 가동율을 보이고 있다. Bit force와 RPM은 (+)의 직선적인 상관관계로 나타났으며 이는 굴착효율에 따라 작업자의 판단에 기인한 결과로 추정된다. 순관입율과의 관계에서는 RBM작업의 bit force와 RPM 및 수직구 심도가 증가함에 따라 순관입율이 저하되는(-)의 상관관계를 나타내었다. 본 연구결과는 수직구 설계 및 RBM장비 선정에 필요한 정보를 줄 수 있을 것으로 사료된다.

• 한국기계기술학회지
• /
• 제20권6호
• /
• pp.929-935
• /
• 2018

This paper develops a flow control block for a hydraulic system of a tunnel boring machine. The flow control block is a necessary component to ensure stability in the operation of the hydraulic system. In order to know the pressure distribution of the flow control block, the flow analysis was performed using the ANSYS-CFX. It was confirmed that the pressure and flow rate were normally supplied to the hydraulic system even if one of the four ports of the flow control block was not operated. In order to evaluate the structural stability of the flow control block, structural analysis was performed using the ANSYS WORKBENCH. As a result, the safety factor of the flow control block is 1.54 and the structural stability is secured.

• 터널과지하공간
• /
• 제13권6호
• /
• pp.413-420
• /
• 2003

본 논문에서는 터널 및 지하공간의 기계화 시공에 있어서 굴진성능을 예측하는 모델링 기법을 고찰하였다. 첫 번째로 세계적으로 가장 잘 알려져 있는 두 가지 모델, 즉 이론적 접근을 기본으로 하고 있는 CSM 모델과 경험적 접근을 기본으로 하고 있는 NTH 모델의 비교를 수행하였다. 두 번째로는, 특별히 Constant Cross Section 커터를 사용하는 경우의 암석 굴삭 원리를 알아보고, 이 원리를 기본으로 하는 이론적 모델을 전개하여 암석특성과 커터 제원만으로 유도되는 절삭력을 구하는 관계식을 고찰하였다. 세 번째로는 기계화 시공에 있어서 굴진성능을 예측하기 위한 일반적인 모델링 기법을 제시하였다. 마지막으로 미국 Colorado School of Mines의 Earth Mechanics Institute(EMI)에서 개발한 CSM 컴퓨터 모델을 소개하고, 이 모델을 TBM 설계에 적용한 사례를 제시하였다.

• Geomechanics and Engineering
• /
• 제39권1호
• /
• pp.27-41
• /
• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• 전기학회논문지
• /
• 제68권1호
• /
• pp.44-51
• /
• 2019

Tunnel Boring Machine's Technology has depends mostly on imports, currently domestic technology development was proceeding. There are many technologies in this field, above all, the large-capacity motor drive technology required for excavation is one of the core technologies. In particular, when several large motors are simultaneously starting, there are many problems due to a large starting current at that time, and it is difficult to design and operate a power receiving facility. In this paper, A method of reducing the starting current by using the regenerative power generated by the deceleration of the motor has been studied. To verify this proposal, we designed the induction motor controller using CAE based power simulation tool and verified the results of the proposed method by applying the reduced model. As a result, it is possible to reduce the maximum starting current and shorten the start-up time. Moreover, even if several motors are connected to one bank, it is proved that the method can be efficiently operated by using the sequential braking / starting sequence. In the case of a power system in which a large capacity electric motor such as a tunnel excavation system is driven, the results of this study are expected to be a stable and effective method for solving the start-up current problem and designing the power receiving facility.

• Geomechanics and Engineering
• /
• 제38권5호
• /
• pp.507-515
• /
• 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.

• 한국산업정보학회논문지
• /
• 제24권6호
• /
• pp.35-42
• /
• 2019

본 논문에서는 TBM(Tunnel boring machine) 시스템의 기동 특성향상을 위해 기존에 제안되었던 회생기동방법에 대한 실제적으로 발생되는 문제점을 분석하고, 이에 대한 해결방법을 제안한다. 기존의 연구결과에서 나타나는 속도리플 문제를 해결하기 위하여, 우선 시스템 상에서의 발생하고 있는 문제점을 분석하고, 이를 보완할 수 있는 방법을 제시하였다. 개선된 방법을 실제 시스템에 적용하여 기존의 시스템 결과와 비교함으로써 제안된 방법이 효과적임을 검증하였다.

• Geomechanics and Engineering
• /
• 제19권1호
• /
• pp.93-104
• /
• 2019

Anomalies and/or fractured grounds not detected by the surface geophysical and geological survey performed during design stage may cause significant problems during tunnel excavation. Many studies on prediction methods of the ground condition ahead of the tunnel face have been conducted and applied in tunneling construction sites, such as tunnel seismic profiling and probe drilling. However, most such applications have focused on the drill and blast tunneling method. Few studies have been conducted for mechanized tunneling because of the limitation in the available space to perform prediction tests. This study aims to predict the ground condition ahead of the tunnel face in TBM tunneling by using an electrical resistivity tomography survey. It compared the characteristics of each electrode array and performed an investigation on in-situ tunnel boring machine TBM construction site environments. Numerical simulations for each electrode array were performed, to determine the proper electrode array to predict anomalies ahead of the tunnel face. The results showed that the modified dipole-dipole array is, compared to other arrays, the best for predicting the location and condition of an anomaly. As the borehole becomes longer, the measured data increase accordingly. Therefore, longer boreholes allow a more accurate prediction of the location and status of anomalies and complex grounds.

• 전기의세계
• /
• 제26권3호
• /
• pp.5-12
• /
• 1977

본 논문에서는 어떻게 레이저의 절삭이 편암의 회전기굴삭에 병용될 수 있는가를 고찰하였다. 특히 레이저 절삭을 병용한 턴넬보오링머시인(tunnel boring machine)과 회전식발파공의 드릴링에 대하여 알아보았다.