• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.1
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• pp.1-12
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• 2016

When utilizing a Tunnel Boring Machine (TBM) for tunnelling work, unexpected ground conditions can be encountered that are not predicted in the design stage. These include fractured zones or mixed ground conditions that are likely to reduce the stability of TBM excavation, and result in considerable economic losses such as construction delays or increases in costs. Minimizing these potential risks during tunnel construction is therefore a crucial issue in any mechanized tunneling project. This paper proposed the potential risk events that may occur due to risky ground conditions. A resistivity survey is utilized to predict the risky ground conditions ahead of the tunnel face during construction. The potential risk events are then evaluated based on their occurrence probability and impact. A TBM risk management system that can suggest proper solution methods (measures) for potential risk events is also developed. Multi-Criterion Decision Making (MCDM) is utilized to determine the optimal solution method (optimal measure) to handle risk events. Lastly, an actual construction site, at which there was a risk event during Earth Pressure-Balance (EPB) Shield TBM construction, is analyzed to verify the efficacy of the proposed system.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.1
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• pp.135-144
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• 2020

Since the depth of tunneling with tunnel boring machine (TBM) becomes deeper and deeper, the expense for site investigation for coring and geophysical survey increases to obtain the sufficient accuracy. The tunnel ahead prediction methods have been introduced to overcome this limitation in the stage of site investigation. Probe drilling can obtain the core and borehole images from a borehole. However, the space in TBM for the probe drilling equipment is restricted and the core from probe drilling cannot reflect the whole tunnel face. Seismic methods such as tunnel seismic prediction (TSP) can forecast over 100 m ahead from the tunnel face though the signal is usually generated using the explosive which can affect the stability of segments and backfill grout. Electromagnetic methods such as tunnel electrical resistivity prospecting system (TEPS) offer the exact prediction for a conductive zone such as water-bearing zone. However, the number of electrodes installed for exploration is limited in small diameter TBM and finally the reduction of prediction ranges. In this study, the theoretical equations for the electrical resistivity survey whose electrodes are installed in the face and side of TBM to minimize the installed electrodes on face. The experimental tests were conducted to verify the derived equations.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.513-527
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• 2018

The Tunnel Boring Machine(TBM) is relatively insufficient to cope with unpredicted changes in ground conditions as compared with Conventional Tunnelling Methods. Therefore, it is very important to predict the TBM performance at the design stage and estimate the advance rate for the calculation of the construction period. In this study, we added data to 211 TBM databases constructed in the previous study and analyzed the correlation between TBM outer diameter, maximum thrust, maximum cutterhead torque, cutterhead driving power and RPM, which are the main design and manufacturing specifications of TBM. As a result of the analysis from results obtained in the previous studies, it was confirmed that TBM outer diameter is very effective and important in estimating maximum thrust, maximum cutterhead torque, and cutterhead driving power of the TBM. As a result of comparing the regression equations derived from other TBM databases outside the country and the regression equation obtained from the present study results, the maximum thrust showed a similar tendency to each other, but the maximum torque estimated from the regression equation of this study was higher than that of other countries in the case of the large scale TBM.

• Structural Engineering and Mechanics
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• v.89 no.1
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• pp.47-59
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• 2024

In this study, a new sustainable material was proposed to prepare precast tunnel lining segments (TLS), which were produced using a fiber-reinforced slag-based geopolymer composite. Slag was used as the geopolymer binder. In addition, polypropylene and carbon fibers were added to reinforce TLSs. TLSs were examined in terms of flexural performance, load-deflection response, ductility, toughness, crack characteristics, and tunnel boring machine (TBM) thrust force. Simultaneously, numerical simulation was performed using finite element analysis. The mechanical characteristics of the geopolymer composite with a fiber content of 1% were used. The results demonstrated that the flexural performance and load-deflection response of the precast TLSs were satisfactory. Furthermore, the numerical results were capable of predicting and realistically capturing the structural behavior of precast TLSs. Therefore, fiber-reinforced slag-based geopolymer composites can be applied as precast TLSs.

• Geotechnical Engineering
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• v.11 no.1
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• pp.51-62
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• 1995

The use of TBM has been rapidly increased in recent years since TBM has been introduced to Korea in 1985 and Korea came to occupy 27% of TBM holding ratio in the world. Despite a lot of experience, study on promoting the efficiency of TBM excavation is insufficient. The factors that influence the efficiency of excavation are the mechanical farttor geotechnical factor and management factor. The study on the efficiency of excavation has focused on the improvement of mechanical factor. But geotechnical factor is also very important and by this factor engineer can estimate the applicability of TBM. The purpose of this paper is to understand the effectiveness of TBM excavation for vari orts rock quality by analysing relations between rock quality and TBM excavation.

• 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.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.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.369-375
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• 2020

The section of this study is the geological vulnerable zone where groundwater leakage occurred through the tunnel barrier during excavation of the shield tunnel boring machine(TBM) for the construction of the electric power unit. Therefore, a Three D imensions(3D) numerical analysis was performed to analyze the actual situation from before construction to the time when the change in groundwater level occurred, and to reflect the surrounding ground conditions based on the observed change in groundwater level during construction. As a result of the study, the correlation between groundwater level change and tunnel construction around the site was identified. Therefore, it was similar to the measurement result of groundwater level at the target ground. The amount of groundwater discharge to the entrance of the tunnel construction was also similar to the actual measured result, and the numerical analysis method and modeling in this study were analyzed to reflect the site conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.