• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1039-1048
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• 2018

In urban tunnel construction, most of the Shield TBM method is applied to secure the safety of buildings and to minimize risks. On the other hand, in the urban development process, landfills are often embanked or improving in many cases, so that the boundary between the surface and the rock is often heterogeneous. In case of ground condition such as alluvial soil, granite, decomposed granite, core stone and rock with various layers, datas on shield TBM advancing according to each ground condition are analyzed, The characteristics of machine load were compared and analyzed. As a result, it can be predicted that the change of ground condition can be predicted by the tendency of discharge volume, thrust force and cutting wheel torque when the cutter is checked and replaced regularly on advancing under maintaining the design slurry pressure.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.387-401
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• 2023

The Earth Pressure Balanced (EPB) Shield Tunnel Boring Machine (TBM) is widely employed for constructing urban underground spaces due to its minimal vibration and low noise levels. The injection of additives offers several advantages, including maintaining shield chamber pressure, reducing shear strength, minimizing cutter wear, and decreasing the permeability of the excavated soil. This technique is known as soil conditioning and involves the application of additives such as foam, polymer, and bentonite slurry. In this study, weathered granite soil commonly encountered at domestic tunnel sites was used as a soil specimen. Foam and polymer were applied as additives to assess the rheological properties of conditioned soils. The workability was evaluated through slump tests, while the rheological properties were assessed through laboratory pressurized vane shear tests conducted under the same conditions. Specially, the polymer was applied under specific conditions with low workability with high slump values, with the aim of evaluating the impact of polymer application. The test results revealed that with an increase in the Foam Injection Ratio (FIR), the slump value also increased, while the torque, peak strength, yield stress, apparent viscosity, and thixotropic area decreased. Conversely, an increase in the Polymer Injection Ratio (PIR) led to results opposite to those of FIR. Additionally, a correlation between the slump value and yield stress was proposed. When comparing conditions with only foam applied to those with both foam and polymer applied, even with similar slump values, the yield stress was found to be lower in the latter conditions.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• 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.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.453-467
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• 2016

Overall risks that can occur in a shield TBM tunnelling are studied in this paper. Both the potential risk events that may occur during tunnel construction and their causes are identified, and the causal relationship between causes and events is obtained in a systematic way. Risk impact analysis is performed for the potential risk events and ways to mitigate the risks are summarized. Literature surveys as well as interviews with experts were made for this purpose. The potential risk events are classified into eight categories: cuttability reduction, collapse of a tunnel face, ground surface settlement and upheaval, spurts of slurry on the ground, incapability of mucking and excavation, and water leakage. The causes of these risks are categorized into three areas: geological, design and construction management factors. Bayesian Networks (BN) were established to systematically assess a causal relationship between causes and events. The risk impact analysis was performed to evaluate a risk response level by adopting an Analytic Hierarchy Process (AHP) with the consideration of the downtime and cost of measures. Based on the result of the risk impact analysis, the risk events are divided into four risk response levels and these levels are verified by comparing with the actual occurrences of risk events. Measures to mitigate the potential risk events during the design and/or construction stages are also proposed. Result of this research will be of the help to the designers and contractors of TBM tunnelling projects in identifying the potential risks and for preparing a systematic risk management through the evaluation of the risk response level and the migration methods in the design and construction stage.