• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.162-169
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• 2003

Excavation by TBM can be characterized by a rock-machine interaction during the cutting process on a small scale, but on a large scale the interaction between the rock mass and TBM becomes very significant. For the planning and evaluation of TBM tunnelling it needs to understand rock fracture mechanism by a cutter or cutters on a small scale, and to estimate penetration rate, advance rate and utilization on a large scale. In this study rock chipping mechanism due to cutter-penetration is analysed by numerical simulation, showing that rock chipping is mainly occurred by tensile failure. Also, through the analysis of factors that affect on TBM procedures in various assessment systems, it is determined that the key elements that should be considered in the planning and evaluation of TBM tunnelling are classified into rock properties, the geological structures and properties of rock mass, and the structural and functional specifications of the machine. The user-friendly assessment tool is developed, so that penetration rate, advance rate and TBM utilization are evaluated from various input data. The tool developed in this study can be applied to a practical TBM tunnelling by understanding TBM tunnelling procedures.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.277-291
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• 2020

Shield TBM is a tunnelling method that has a wider range of applications in the poor ground condition compared to conventional tunnels (Drill and Blast). Currently, a 13.3 m large-diameter slurry shield TBM is preparing for construction to pass under the Han River. Shield TBM is divided into slurry and EPB shield TBM, and management items during construction are different depending on each characteristic. In this paper, the equipment type, origin, application case and trouble case were analyzed for slurry shield TBM, which is mainly constructed in soft ground. In addition, 2D and 3D model tests were conducted on the condition of soil depth for the possibility of slurry leakage into front of the equipment, with appropriate chamber pressure. Based on this paper, it proposed to provide basic and reference data for proper excavation surface pressure and chamber pressure during construction of slurry shield TBM under soft ground conditions, and proposed measures to minimize stability and environmental decline due to slurry ejection.

• Explosives and Blasting
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• v.41 no.4
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• pp.9-16
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• 2023

To compare the global warming impact of the TBM and NATM method, which are representative tunnel excavation methods, a life cycle assessment was performed for each method. Life cycle assessment should compare the sum of carbon emissions by considering the pre-manufacturing stage, product manufacturing stage, usage stage, and disposal stage. However, access to TBM (Tunnel Boring Machine) manufacturing and disposal data is limited, so I had no choice but to focus on the analysis for the usage stage. In general, carbon emissions during the pre-product manufacturing stage and product manufacturing stage often exceed 90% of carbon emissions throughout the entire process. Therefore, since it is difficult to achieve the analysis goal only by comparing the usage stage, the analysis scope was expanded, and carbon emissions for the process were calculated for the NATM method with access to manufacturing data. As a result of comparing the relative impact on global warming, the carbon emissions of the TBM method were found to be higher than those of the NATM method even though TBM method was only considered for the usage stage. So there it is, the NATM method can be seen as environmentally friendly in the future when considering the impact of climate change (global warming), which has recently attracted attention among environmental impact fields.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.641-656
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• 2019

In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.513-525
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• 2018

A 3 dimensional numerical analysis for shield TBM tunnel should take into account various characteristics of the shield TBM excavation, such as gap, tail void, segment installation, and backfill injection. However, analysis method considering excavation characteristics are generally mixed with various method, resulting in concern of consistency and reliability degradation of the analytical results. In this paper, a parametric study is carried out by using actually measured ground settlement data on various methods that can be used for 3 dimensional numerical analysis of shield TBM tunneling. As a result, we have analyzed and arranged an analytical method to predict similarly the behavior of ground settlement and tunnel face pressure at the design stage. Skin plate pressure, backfill pressure and soil model have been identified as the most significant influences on the ground settlement. The grout pressure model is considered to be applicable when there is no volume loss information on the excavated ground, such as seabed tunnels, or when it is important to identify the behavior around a tunnel, such as surface settlement as well as face pressure. And it is considered that designers can use these guidelines as a base material to perform a reasonable 3 dimensional numerical analysis that reflects the ground conditions and the features of the shield TBM tunneling.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Journal of the Korean GEO-environmental Society
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• v.20 no.2
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• pp.29-40
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• 2019

Mechanized constructions have been frequently increased in soft ground below sea bed or river bed, for urban tunnel construction, and for underpinning the lower part of major structures in order to construct a safer tunnel considering various risk factors during the tunnel construction. However, it is difficult to estimate the subsidence behavior of the ground surface due to excavation and needs to be easily predicted. Thus, in this study, when a twin tunnel is constructed in the soft ground, it is proposed a simpler equation relating to the settlement behavior and a corrected formula applicable to soft ground and large diameter shield tunnels based on the previously proposed theory by Peck (1969). For this purpose, it was analyzed to long-term measurement values such as the amount of maximum settlement, the subsidence range by ground conditions, and interference volume loss due to the parallel construction, etc. As a result, a equation was suggested to predict the amount of maximum settlement in the soft sediment clay ground where is located at the upper part of the excavation site. It is turned out that the proposed equation is more suitable for measurement data in Korea than Peck (1969)'s.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.69-77
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• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.