• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.385-392
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• 2021

When ordering a slurry shield TBM to be used for power cable tunneling, the client organizes an evaluation committee composed of experts, suggest the criteria and evaluation method for technical specifications for supplier selection, and based on the manufacturer's technical proposal were attempted to evaluate and select. It is expected to be referred to as a guideline for future projects to using Shield TBM as one of the methods of verifying performance and quality in advance and securing economic feasibility in the shield TBM tunneling in the recent increasing trend.

• 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.

• Magazine of korean Tunnelling and Underground Space Association
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• v.19 no.2
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• pp.39-51
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• 2017

• Magazine of korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.29-35
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• 2022

• 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 Korean Tunnelling and Underground Space Association
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• v.20 no.4
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• pp.687-699
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• 2018

The construction of subsea tunnel differs from that of inland tunnel because of high water pressure due to sea water level and difficulties to reinforce the ground under construction. Therefore, it is very important to prevent trouble in advance when the subsea tunnel is constructed. In this paper, we established lots of databases about characteristics of geological and mechanical parameters on the construction of subsea tunnel using micro slurry TBM which depth is about 60 m. The correlation analysis is conducted to confirm the effect of thrust, torque and RPM among the excavation database on the net penetration rate. Also, An empirical formula is suggested to predict the net penetration rate through the correlation analysis between FPI (Field Penetration Index) and specific energy from the subsea tunnel excavation database.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1039-1048
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• 2018

In urban tunnel construction, most of the Shield TBM method is applied to secure the safety of buildings and to minimize risks. On the other hand, in the urban development process, landfills are often embanked or improving in many cases, so that the boundary between the surface and the rock is often heterogeneous. In case of ground condition such as alluvial soil, granite, decomposed granite, core stone and rock with various layers, datas on shield TBM advancing according to each ground condition are analyzed, The characteristics of machine load were compared and analyzed. As a result, it can be predicted that the change of ground condition can be predicted by the tendency of discharge volume, thrust force and cutting wheel torque when the cutter is checked and replaced regularly on advancing under maintaining the design slurry pressure.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.4
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• pp.401-413
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• 2013

Slurry shield tunnelling ensures stability by pressurizing the tunnel face with the slurry contained in the chamber. It resists water and earth pressure in order to prevent the failure in the tunnel face during tunnel excavation. If the ground is relatively coarse, slurry can not clog the tunnel face and excessive slurry infiltration will occur. In this case chemical compounds or additives should be added to the slurry in order to improve the clogging phenomena at the tunnel face. In this study, the effect of the carbon dioxide gas as an additive to the slurry instead of chemical compounds on the capability of enhancing the clogging in the tunnel face is investigated. Bubbles arising from the carbonate-added slurry are trapped in the soil voids enhancing the clogging capability. This effect is studied in this paper by performing laboratory model tests simulating in-situ conditions, and by adopting the fine particle clogging theory. Tunnel face stability analysis was also performed and it was found that the effective size ($D_{10}$) of soils which can guarantee tunnel stability utilizing the carbonate-added slurry increased from 1.0 mm up to 2.6 mm. Moreover, Stability analysis showed that the tunnel face is stable if the ${\lambda}$(deposition coefficient) value is greater than $0.007sec^{-1}$.