• 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.

• 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.

• 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.

• Journal of the Society of Disaster Information
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• v.8 no.2
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• pp.108-118
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• 2012

The application of TBM tunneling has been progressively increased since the first entrance into korea in 1985. In order to an apprehension and operation development of TBM method which has excellent boring in hard rock tunnel, this study has analyzed mutual relation of lose time and TBM boring from actual construction results TBM tunneling. This study compared this analyzed results with TBM tunnelling construction results of korea, america, japan, analyzed a primary factor of TBM boring effect and suggested operation development method from the analyzed results. accordingly this study can be used an index when contract apply TBM method to planning steps.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.301-310
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• 2022

Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FE model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FE numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.

• Geomechanics and Engineering
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• v.24 no.4
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• pp.323-335
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• 2021

This paper aims to estimate the range of the excavation damaged zone (EDZ) formation caused by the tunnel boring machine (TBM) advancement through dynamic three-dimensional large deformation finite element analysis. Large deformation analysis based on Coupled Eulerian-Lagrangian (CEL) analysis is used to accurately simulate the behavior during TBM excavation. The analysis model is verified based on numerous test results reported in the literature. The range of the formed EDZ will be suggested as a boundary under various conditions - different tunnel diameter, tunnel depth, and rock type. Moreover, evaluation of the integrity of the tunnel structure during excavation has been carried out. Based on the numerical results, the apparent boundary of the EDZ is shown to within the range of 0.7D (D: tunnel diameter) around the excavation surface. Through series of numerical computation, it is clear that for the rock of with higher rock mass rating (RMR) grade (close to 1st grade), the EDZ around the tunnel tends to increase. The size of the EDZ is found to be direct proportional to the tunnel diameter, whereas the depth of the tunnel is inversely proportional to the magnitude of the EDZ. However, the relationship between the formation of the EDZ and the stability of the tunnel was not found to be consistent. In case where the TBM excavation is carried out in hard rock or rock under high confinement (excavation under greater depth), large range of the EDZ may be formed, but less strain occurs along the excavation surface during excavation and is found to be more stable.

• 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.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.7-20
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• 2003

• Explosives and Blasting
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• v.13 no.1
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• pp.46-51
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• 1995