• Tunnel and Underground Space
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• v.30 no.5
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• pp.496-507
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• 2020

A prediction of the performance of EPB TBM is significant for improving the constructability of tunnels. Thus, various attempts to simulate TBM excavation by the numerical method have been made until these days. In this paper, to evaluate the performance of TBM with different operating conditions, a parametric study was carried out using coupled discrete element method (DEM) and finite difference method (FDM) EPB TBM driving model. The analysis was conducted by changing the penetration rate (0.5 and 1.0 mm/sec) and the rotational speed of screw conveyor (5, 15, and 25 rpm) while the rotation velocity of the cutter head kept constant at 2 rpm. The torque, thrust force, chamber pressure, and discharging with different TBM operating conditions were compared. The result of parametric study shows that the optimum driving condition can be determined by the coupled DEM-FDM numerical model.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.484-495
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• 2020

To numerically simulate the advance of EPB TBM, various type of numerical analysis methods have been adopted including discrete element method (DEM), finite element method (FEM), and finite difference method (FDM). In this paper, an EPB TBM driving model was proposed by using coupled DEM-FDM. In the numerical model, DEM was applied in the TBM excavation area, and contact properties of particles were calibrated by a series of triaxial tests. Since the ground around the excavation area was coupled with FDM, the horizontal stress considering the coefficient of earth pressure at rest could be applied. Also, the number of required particles was reduced and the efficiency of the analysis was increased. The proposed model can control the advance rate and rotational speed of the cutter head and screw conveyor, and derive the torque, thrust force, chamber pressure, and discharging during TBM tunnelling.

• Tunnel and Underground Space
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• v.29 no.5
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• pp.281-291
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• 2019

In Overseas case, most tunnels in under are or through the river are constructed with shield TBM and the manufacturer orders of related equipment suitable for the project are mode. Accordingly, the client provides the specifications required for the equipment manufacture. In addition, TBM equipment has been operated by those who have completed the expert training program, which for minimizing the risk of equipment operation in construction field corresponding to the mechanized construction. However, in Korea, such a system related to above the program and specifications has not yet been built, which is causing a lot of difficulties in construction field. Therefore, this study investigated the differences in bidding guides provided by mechanized construction in domestic and abroad, and the professional education programs for expert training being conducted from overseas. Futhermore, we will propose the guidelines of essential equipment specification contained in domestic bidding and provide the necessary manual for the professional education program for TBM as the mechanized construction method.

• 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.