• Proceedings of the KSR Conference
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• 2003.10b
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• pp.569-578
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• 2003

In this paper a determination of the optimal excavation method and machine type for a tunnel under the Han river between the Sungsoo-dong, Sungdong-Gu and the Chungdaw-dong, Kangnam-Gu in the Bundang railway. The geological investigation results show that some fractured zones exist locally under the northern boundary of the Han river bed, but the other regions consist mostly of hard rocks of good quality in the tunnel excavation level. Also, a hign water pressure of $5kgf/cm^2$ and a flash inflow of river water due to old boring holes are expected during tunnel excavation. A EPB shield TBM is selected as a optimal excavation machine for the Han river tunnel considering the geological and ,site conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.5-96
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• 2001

The probe with a 110mm diameter is originated and fabricated to measure hole accuracies of extremely deepholes. It consists of a measuring unit, an actuator unit, an active rotation stopper and a feed unit. The rolling of the probe is restricted and adjusted by the active rotation stopper. The probe is fed by the feed unit. In this measurement, accuracies are measured by using a rolling proof apparatus and machine table of deep hole boring machine instead of the stopper and the feed unit, respectively. Straightness, roundness and a diameter of a 110-mm hole are measured by the probe and testers made for each measuring purpose ...

• Tunnel and Underground Space
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• v.4 no.2
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• pp.144-155
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• 1994

This research is designed to assess current achievement levels for mechanized excavation systems in Korea adn suggest the model predictive of TBM performance using statistical approaches. A test section in the TBM construction sites is selected to measure and analyze TBM performance. The field records including operating data, time allocation into downtime catagories, and machine design are analyzed on a shift basis. There are a total of 240 shifts, with most days operating two shifts per day. Examples of the probability density functions produced from the test section are presented and discussed. Relationships between TBM penetration rate and rock physical properties are investigated and the empirical equations for TBM performance prediction are also assessed with the field data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.5
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• pp.48-52
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• 2019

A coolant pump is the device that cools processed articles and tools when using cutting, boring, and grinding machine tools and provides cutting oil for distributing or cleansing the cut chip to the worktable, processing position, etc. In particular, it consumes a large proportion of energy in machine tools, so it plays an important role in terms of energy efficiency. The purpose of this research is to optimize the shape of impeller, which directly affects performance improvements, to determine the capacity of the coolant pump. To do so, we carried out a parametric analysis with the geometric shape of the impeller as the input variable.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• Geomechanics and Engineering
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• v.38 no.6
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• pp.633-645
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• 2024

Disc cutters, used as excavation tools for rocks in a Tunnel Boring Machine (TBM), naturally undergo wear during the tunneling process, involving crushing and cutting through the ground, leading to various wear types. When disc cutters reach their wear limits, they must be replaced at the appropriate time to ensure efficient excavation. General disc cutter life prediction models are typically used during the design phase to predict the total required quantity and replacement locations for construction. However, disc cutters are replaced more frequently during tunneling than initially planned. Unpredictable disc cutter replacements can easily diminish tunneling efficiency, and abnormal wear is a common cause during tunneling in complex ground conditions. This study aims to overcome the limitations of existing disc cutter life prediction models by utilizing machine data generated during tunneling to predict disc cutter wear patterns and determine the need for replacements in real-time. Artificial intelligence classification algorithms, including K-nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Stacking, are employed to assess the need for disc cutter replacement. Binary classification models are developed to predict which disc cutters require replacement, while multi-class classification models are fine-tuned to identify three categories: no replacement required, replacement due to normal wear, and replacement due to abnormal wear during tunneling. The performance of these models is thoroughly assessed, demonstrating that the proposed approach effectively manages disc cutter wear and replacements in shield TBM tunnel projects.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.558-564
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• 2011

This study was conducted to evaluate the effects of early revegetation by analyzing the characteristics of germination and growth of Lespedeza cyrtobotrya using boring technique in rock slopes. After making up a growing basis of approximately 20 cm depth and 10 cm diameter by using a boring machine, the surface of rock slopes was planted with vegetation-plant. The number of germinating populations by soil media was 23 in H.s, 22 in T.s, 12 in M.s, and 1 in M.g.s. The germination rate (%) by soil media was 19.2% in H.s, 18.3% in T.s, 10.0% in M.s and 0.8% in M.g.s. In monthly changes of growth rate, the aspect was northwest direction, the soil media was M.s, and the treatment was microorganism plot. The main factors affecting survivorship and growth of population were soil media and treatment plot. The interaction between each factor had a good effects in bearing ${\times}$ soil media, bearing ${\times}$ treatment plot, soil media ${\times}$ treatment plot. but, it is recommended that the mulching of vegetation plant is highly needed to help the germination of seed and growth of vegetation because of loss of seed and soil media occurred due to rainfall. Therefore, The result suggests that the revegetation technique using boring in rock slope was very efficient in respect of the early revegetation and the landscape.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• 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.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.