• Journal of the Korean Professional Engineers Association
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• v.24 no.5
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• pp.77-80
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• 1991

• Tunnel and Underground Space
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• v.13 no.3
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• pp.196-206
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• 2003

In order to investigate the workability of Raise Boring Machine(RBM) such as utilization, penetration rate and advance rate, a vertical shaft of 98 m in length and 3.05 m in diameter was constructed in the layer of conglomerate by using the RBM in this study. In addition, field data from tow different construction sites including water-pump power plant tunnel, roadway tunnel and mining tunnel by RBM were collected and analyzed. The results show that the average weekly bored length is 19.3 m and its average utilization is between 54.3 % and 75.1 % very higher than that of the TBM(Tunnel Boring Machine). It also turns out that the bit force increases linearly with respect to the increase of the RPM(revolution per minute) of RBM. However, the net penetration rate decreases with the increase of bit force, RPM of RBM and depth of shaft. The findings of this study can be used to provide the useful information for the design of shaft and the selection of RBM.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.929-935
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• 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.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.413-420
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• 2003

Modelling for performance prediction of mechanical excavation is discussed in this paper. Two of the most successful performance prediction models, namely theoretical based CSM model and empirical based NTH model, are discussed and compared. The basic principles of rock cutting with disc cutters, especially Constant Cross Section cutters, are discussed and a theoretical model developed is introduced to provide an estimate of disc cutting forces as a function of rock properties and the cutting geometry. General modelling logic for the performance prediction of mechanical excavation is introduced. CSM computer model developed and currently used at the Earth Mechanics Institute(EMI) of the Colorado School of Mines is discussed. Example of input and output of this model is illustrated for the typical operation by Tunnel Boring Machine(TBM).

• Geomechanics and Engineering
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• v.39 no.1
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• pp.27-41
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• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.44-51
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• 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
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.35-42
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• 2019

In this paper, we analyze the practical problems of the regenerative start method proposed to improve the starting characteristics of the TBM(Tunnel boring machine)system and propose a solution. In order to solve the speed ripple problem in the previous research results, we first analyze the problems occurring in the system and propose a method to compensate for them. By applying the improved method to the actual system, we compared the results with the conventional system and verified the effect of the proposed method.

• Geomechanics and Engineering
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• v.19 no.1
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• pp.93-104
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• 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
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• v.26 no.3
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• pp.5-12
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• 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.