• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.2
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• pp.245-256
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• 2016

Prediction of TBM performance is very important for machine selection, and for reliable estimation of construction cost and period. The purpose of this research is to analyze the evaluation process of various prediction models for TBM performance and applied methodology. Based on the solid literature review since 2000, a classification system of TBM performance prediction model is proposed in this study. Classification system suggested in this study can be divided into two stages: selection of input parameter and application of prediction techniques. We also analyzed input and output parameters for prediction model and frequency of use. Lastly, the future research and development trend of TBM performance prediction is suggested.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.553-581
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• 2022

Shortly after tunnel boring machine (TBM) was introduced in the tunneling industry, the use of TBM has surprisingly increased worldwide due to its performance together with the benefit of being safely and environmentally friendly. One of the main cost items in the TBM tunneling in rock and soil is changing damaged or worn cutters. It is because that the cutter change is a time-consuming and costly activity that can significantly reduce the TBM utilization and advance rate and has a major effect on the total time and cost of TBM tunneling projects. Therefore, the importance of accurately evaluating the cutter life can never be overemphasized. However, the prediction of cutter wear in soil, rock including mixed face is very complex and not yet fully clarified, subsequently keeping engineers busy around the world. Various prediction models for cutter wear have been developed and introduced, but these models almost usually produce highly variable results due to inherent uncertainties in the models. In this study, a case study of design and construction of disc cutter change is introduced and analyzed, rather than proposing a prediction model of cutter wear. As the disc cutter is strongly affected by the geological condition, TBM machine characteristic and operation, authors believe it is very hard to suggest a generalized prediction model given the uncertainties and limitations therefore it would be more practical to analyze a real case and provide a detailed discussion of the difference between prediction and result for the cutter change. By doing so, up-to-date idea about planning and execution of cutter change in practice can be promoted.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.403-421
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• 2023

As the number of underground structures has increased in recent decades, it has become crucial to predict geological hazards ahead of a tunnel face during tunnel construction. Consequently, this study developed a finite element (FE) numerical model to simulate electrical resistivity surveys in tunnel boring machine (TBM) operations for predicting mixed ground conditions in front of tunnel faces. The accuracy of the developed model was verified by comparing the numerical results not only with an analytical solution but also with experimental results. Using the developed model, a series of parametric studies were carried out to estimate the effect of geological conditions and sensor geometric configurations on electrical resistivity measurements. The results of these studies showed that both the interface slope and the difference in electrical resistivity between two different ground formations affect the patterns and variations in electrical resistivity observed during TBM excavation. Furthermore, it was revealed that selecting appropriate sensor spacing and optimizing the location of the electrode array were essential for enhancing the efficiency and accuracy of predictions related to mixed ground conditions. In conclusion, the developed model can serve as a powerful and reliable tool for predicting mixed ground conditions during TBM tunneling.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.533-549
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• 2024

Soil conditioning is a critical process when tunneling with an earth pressure balance (EPB) shield tunnel boring machine (TBM) to enhance performance. To determine the optimal additive injection conditions, it is important to understand the rheological properties of conditioned soil, which is typically assessed using a rheometer. However, a rheometer cannot simulate the actual process of muck discharge in a TBM. Therefore, in this study, a scaled-down model of an 8-meter-class EPB shield TBM chamber and screw conveyor, reduced by a factor of 1:20, was fabricated and its applicability was evaluated through laboratory experiments. A lab-scale model experiment was conducted on artificial sandy soil using foam and polymer as additives. The experimental results confirmed that screw torque was consistent with trends observed in previous laboratory pressurized vane shear test data, establishing a positive proportional relationship between screw torque and yield stress. The muck discharge efficiency according to foam injection ratio (FIR) showed similar values overall, but decreased slightly at 60% of FIR and when the polymer was added. In addition, the pressure distribution generated along the chamber and screw conveyor was assessed in a manner similar to the actual EPB TBM. This study demonstrates that the lab-scale screw conveyor model can be used to evaluate the shear properties and muck discharge efficiency.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.181-187
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• 2003

This paper describes a study of the influence of shield tunnel construction on the displacements and stresses induced in the linings of existing nearby parallel tunnels. The paper presents a brief review of a set of laboratory scale model research programme investigating the influence of tunnel proximity and alignment, liner stiffness on the nature of the interactions between closely spaced tunnels in clay. A total of two sets of carefully controlled physical model tests were performed. A cylindrical test tank was developed and used to produce clay samples of Speswhite kaolin. In each of the tests, three model tunnels were installed in order to conduct two interaction exts that have been carried out to investigate the interaction problem between parallel tunnels. The results of these tests are compared with the results of finite element analysis to investigate the techniques that must be used to obtain reliable numerical solutions to this type of problem.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.219-237
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• 2020

Rapid economic development and urban population growth have been increasing the necessity for underground space exploration and utilization due to the need of upgrading and expanding the existing infrastructures. TBM has been widely used to construct underground structures with high advance rate and minimal ground disturbance. Two important design parameters, which are available thrust capacity and cutterhead torque, should be estimated for any project in addition to proper selection of TBM type. However, the conventional thrust force and torque estimation model only depends on the empirical equation, which hinders the design process of the optimal thrust hydraulic system and the appropriate hydraulic components. In this study, four thrust and torque calculation models are derived and explained. For TBM design practice, the four estimation models are compared and discussed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• 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.22 no.5
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• pp.501-513
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• 2020

Cutter cutting tests for the cutter placement in the cutter head are being conducted through various studies. Although the cutter spacing at the minimum specific energy is mainly reflected in the cutter head design, since the optimum cutter spacing at the same cutter penetration depth varies depending on the rock conditions, studies on deciding the optimum cutter spacing should be actively conducted. The machine learning techniques such as the decision tree-based regression model and the SVM regression model were applied to predict the optimum cutter spacing ratio for the nonlinear relationship between cutter penetration depth and cutter spacing. Since the decision tree-based methods are greatly influenced by the number of data, SVM regression predicted optimum cutter spacing ratio according to the penetration depth more accurately and it is judged that the SVM regression will be effectively used to decide the cutter spacing when designing the cutter head if a large amount of data of the optimum cutter spacing ratio according to the penetration depth is accumulated.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.