• Journal of the Korean Geotechnical Society
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• v.37 no.10
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• pp.55-63
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• 2021

The construction of shield tunnels is being expanded from the small-bore tunnels such as power, telecommunications, water supply, and sewerage pipes to the large bore tunnels such as road and railway tunnels with the advancement of the shield TBM (Tunnel Boring Machine) manufacturing technology. Accordingly, the construction of twin shield tunnels is increasing. Peck (1969) reported that the surface settlement trough is well described by a Gaussian distribution on a single shield tunnel. Hereafter, many studies about the surface settlement trough were performed by the field measurement, laboratory model test, and numerical analysis. This study confirmed that the additional surface settlement trough induced by the 2^nd tunneling were well described by using each Gaussian curve for the right and left sides of the settlement trough.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.244-254
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• 2023

The disc cutter mounted on the Tunnel Boring Machine (TBM) is subjected to cutting forces in three dimensions during rock excavation process. It is widely known that the cutting forces increased with the strength of the rock mass, while the rolling force can be significantly increased when the disc cutter encounters abnormal rotation. Therefore, the cutting force acts on the disc cutter provides important information because it represents the conditions of the rock mass and the disc cutter. For these reasons, several studies have been conducted to measure the cutter forces in real-time. This paper introduces the current status of research on the cutter force measurement of TBM disc cutters, which has been reported in the literature. It is judged that this paper can be a useful reference material when similar technologies are developed in Korea in the future.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.103-113
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• 2023

This study focused on understanding the relationship between the design of a tunnel boring machine disc cutter ring and its rock-breaking efficiency, as well as the applicable conditions of different cutter ring types. The discrete element method was used to establish a numerical model of the rock-breaking process using disc cutters with different ring types to reveal the development of rock damage cracks and variation in cutter penetration load. The calculation results indicate that a sharp-edged (V-shaped) disc cutter penetrates a rock mass to a given depth with the lowest load, resulting in more intermediate cracks and few lateral cracks, which leads to difficulty in crack combination. Furthermore, the poor wear resistance of a conventional V-shaped cutter can lead to an exponential increase in the penetration load after cutter ring wear. In contrast, constant-cross-section (CCS) disc cutters have the highest quantity of crack extensions after penetrating rock, but also require the highest penetration loads. An arch-edged (U-shaped) disc cutter is more moderate than the aforementioned types with sufficient intermediate and lateral crack propagation after cutting into rock under a suitable penetration load. Additionally, we found that the cutter ring wedge angle and edge width heavily influence cutter rock-breaking efficiency and that a disc cutter with a 16 to 22 mm edge width and 20° to 30° wedge angle exhibits high performance. Compared to V-shaped and U-shaped cutters, the CCS cutter is more suitable for soft or medium-strength rocks, where the penetration load is relatively small. Additionally, two typical case studies were selected to verify that replacing a CCS cutter with a U-shaped or optimized V-shaped disc cutter can increase cutting efficiency when encountering hard rocks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2C
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• pp.125-131
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• 2008

Ground stiffness(shear wave velocity) is one of the key parameters in geotechnical earthquake engineering. An In-situ seismic technique has its own advantages and disadvantages over the others in stiffness measurements. By combining the crosshole and seismic cone techniques and utilizing favourable features of bender elements, a new hybrid probe has been developed in order to enhance data quality and easiness of testing. The basic structure of the probe, called "MudFork" is a fork composed of two blades, on each of which source and receiver bender elements were mounted respectively. To evaluate the disturbance caused by the penetration of the probe, shear wave velocity measurements were carried out in the Kaolinite slurry in the laboratory. Finally, the probe was penetrated in coastal mud near Incheon, Korea, using SPT(standard penetration test)rods pushed with a routine boring machine and shear wave velocity measurements were carried out. The results were verified with data from laboratory and cone testing. The performance of the probe turns out to be excellent in terms of data quality and testing convenience.

• Geomechanics and Engineering
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• v.35 no.5
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• pp.487-497
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• 2023

Penetration rate (PR) and penetration depth (Pe) are crucial parameters for estimating the cost and time required in tunnel construction using tunnel boring machines (TBMs). This study focuses on investigating the impact of rock strength on PR and Pe through full-scale experiments. By conducting controlled tests on rock-like specimens, the study aims to understand the contributions of various ground parameters and machine-operating conditions to TBM excavation performance. An earth pressure balanced (EPB) TBM with a sectional diameter of 3.54 m was utilized in the experiments. The TBM excavated rocklike specimens with varying uniaxial compressive strength (UCS), while the thrust and cutterhead rotational speed were controlled. The results highlight the significance of the interplay between thrust, cutterhead speed, and rock strength (UCS) in determining Pe. In high UCS conditions exceeding 70 MPa, thrust plays a vital role in enhancing Pe as hard rock requires a greater thrust force for excavation. Conversely, in medium-to-low UCS conditions less than 50 MPa, thrust has a weak relationship with Pe, and Pe becomes directly proportional to the cutterhead rotational speed. Furthermore, a strong correlation was observed between Pe and cutterhead torque with a determination coefficient of 0.84. Based on these findings, a predictive model for Pe is proposed, incorporating thrust, TBM diameter, number of disc cutters, and UCS. This model offers a practical tool for estimating Pe in different excavation scenarios. The study presents unprecedented full-scale TBM excavation results, with well-controlled experiments, shedding light on the interplay between rock strength, TBM operational variables, and excavation performance. These insights are valuable for optimizing TBM excavation in grounds with varying strengths and operational conditions.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.451-468
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• 2021

Predicting hazardous ground conditions ahead of a TBM (Tunnel Boring Machine) tunnel face is essential for efficient and stable TBM advance. Although there have been several studies on the electrical resistivity survey method for TBM tunnelling, sufficient experimental data considering TBM advance were not established yet. Therefore, in this study, the laboratory-scale model experiments for simulating TBM excavation were carried out to analyze the applicability of an electrical resistivity survey for predicting hazardous ground conditions ahead of a TBM tunnel face. The trend of electrical resistivity during TBM advance was experimentally evaluated under various hazardous ground conditions (fault zone, seawater intruded zone, soil to rock transition zone, and rock to soil transition zone) ahead of a tunnel face. In the course of the experiments, a scale-down rock ground was provided using granite blocks to simulate the rock TBM tunnelling. Based on the experimental data, the electrical resistivity tends to decrease as the tunnel approaches the fault zone. While the seawater intruded zone follows a similar trend with the fault zone, the resistivity value of the seawater intrude zone decreased significantly compared to that of the fault zone. In case of the soil-to-rock transition zone, the electrical resistivity increases as the TBM approaches the rock with relatively high electrical resistivity. Conversely, in case of the rock-to-soil transition zone, the opposite trend was observed. That is, electrical resistivity decreases as the tunnel face approaches the rock with relatively low electrical resistivity. The experiment results represent that hazardous ground conditions (fault zone, seawater intruded zone, soil-to-rock transition zone, rock-to-soil transition zone) can be efficiently predicted by utilizing an electrical resistivity survey during TBM tunnelling.

• Tunnel and Underground Space
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• v.21 no.6
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• pp.508-517
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• 2011

The linear cutting test is the most reliable and accurate approach to measuring cutting forces and cutting efficiency using full-size disc cutter in various rock types. The result of linear cutting tests can be used to obtain the key parameters of cutter-head design (i.e. optimum cutter spacing, cutter forces). In Korea, LCM (Linear Cutting Machine) tests have been performed for typical Korean rock types, but these studies focused on the isotropic rocktypes. For prediction of TBM (Tunnel Boring Machine) performances in complex geological conditions including a bedded and schistose rockmass, it is important to consider the effects of anisotropy of rockmass on cutting performances and cutting efficiency. This study discusses a series of LCM tests that were performed for Asan Gneiss having two types of anisotropy angles to assess the effect of the anisotropy angle on rock-cutting performances of TBM. The result shows that the rock-cutting performances and optimum cutting conditions are affected by anisotropy angle and the effect of anisotropy on rock strength should be considered in a prediction of the cutting performances and efficiency of TBM.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• 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.