• KEPCO Journal on Electric Power and Energy
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• v.6 no.3
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• pp.305-313
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• 2020

The use of cable tunnels for electric power transmission as well as their construction in difficult conditions such as in subsea terrains and large overburden areas has increased. So, in order to efficiently operate the small diameter shield TBM (Tunnel Boring Machine), the estimation of advance rate and development of a design model is necessary. However, due to limited scope of survey and face mapping, it is very difficult to match the rock mass characteristics and TBM operational data in order to achieve their mutual relationships and to develop an advance rate model. Also, the working mechanism of previously utilized linear cutting machine is slightly different than the real excavation mechanism owing to the penetration of a number of disc cutters taking place at the same time in the rock mass in conjunction with rotation of the cutterhead. So, in order to suggest the advance rate and machine design models for small diameter TBMs, an EPB (Earth Pressure Balance) shield TBM having 3.54 m diameter cutterhead was manufactured and 19 cases of full-scale tunneling tests were performed each in 87.5 ㎥ volume of artificial rock mass. The relationships between advance rate and machine data were effectively analyzed by performing the tests in homogeneous rock mass with 70 MPa uniaxial compressive strength according to the TBM operational parameters such as thrust force and RPM of cutterhead. The utilization of the recorded penetration depth and torque values in the development of models is more accurate and realistic since they were derived through real excavation mechanism. The relationships between normal force on single disc cutter and penetration depth as well as between normal force and rolling force were suggested in this study. The prediction of advance rate and design of TBM can be performed in rock mass having 70 MPa strength using these relationships. An effort was made to improve the application of the developed model by applying the FPI (Field Penetration Index) concept which can overcome the limitation of 100% RQD (Rock Quality Designation) in artificial rock mass.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.857-870
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• 2017

The use of shield TBM is gradually increasing due to the urbanization of social infrastructures. Reliable estimation of advance rate is very important for accurate construction period and cost. For this purpose, it is required to develop the prediction model of advance rate that can consider the ground properties reasonably. Based on the database collected from field, statistical prediction procedure for field penetration index (FPI) was modularized in this study to calculate penetration rate of shield TBM. As output parameter, FPI was selected and various systems were included in this module such as, procedure of eliminating abnormal dataset, preprocessing of dataset and ridge regression with best subset selection. And it was finally validated by using field dataset.

• Magazine of korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.29-35
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• 2022

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.217-230
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• 2022

The adequate control of TBM face pressure is of vital importance to maintain face stability by preventing face collapse and surface settlement. An EPB shield TBM excavates the ground by applying face pressure with the excavated soil in the pressure chamber. One of the challenges during the EPB shield TBM operation is the control of face pressure due to difficulty in managing the excavated soil. In this study, the face pressure of an EPB shield TBM was predicted using the geological and operational data acquired from a domestic TBM tunnel site. Four machine learning algorithms: KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), and XGB (eXtreme Gradient Boosting) were applied to predict the face pressure. The model comparison results showed that the RF model yielded the lowest RMSE (Root Mean Square Error) value of 7.35 kPa. Therefore, the RF model was selected as the optimal machine learning algorithm. In addition, the feature importance of the RF model was analyzed to evaluate appropriately the influence of each feature on the face pressure. The water pressure indicated the highest influence, and the importance of the geological conditions was higher in general than that of the operation features in the considered site.

• 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.24 no.1
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• pp.57-78
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• 2022

The disc cutter and cutterbit, which are the most important factors to increase the excavation efficiency of TBM, are key factors in the design and construction of the cutter head. The arrangement, spacing, number, size, and material of disc cutters suitable for the ground conditions determine the success or failure of TBM construction. The disc cutter, which is a representative consumable part in TBM construction, can cause enormous disruption to the construction cost as well as the construction cost unless accurate prediction of wear and replacement cycle is accompanied. Therefore, in this study, the method of calculating the replacement cycle of the disc cutter calculated at the time of design for the slurry shield TBM field, and the depth of wear and replacement location of the disc cutter that occurred during actual construction were compared by analyzing the field data. For a quantitative comparison, weathered soil/weathered rock, soft rock, and hard rock were classified according to the ground in the section showing constant excavation data, and the trajectory of circle was different depending on the location of the disc cutter, so it was compared and analyzed.

• 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.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.355-374
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• 2022

Soil conditioning improves the performance of EPB (earth pressure balance) shield TBMs (tunnel boring machines) by reducing shear strength, enhancing workability of the excavated soil, and supporting the tunnel face during EPB tunnelling. The mechanical and rheological behavior of the excavated muck mixed with additives should be properly evaluated to determine the optimal additive injection condition corresponding to each ground type. In this study, the laboratory pressurized vane test apparatus equipped with a vane-shaped rheometer was developed to reproduce the pressurized condition in the TBM chamber and quantitively evaluate rheological properties of the soil specimens. A series of the pressurized vane tests were performed for an artificial sand soil by changing foam injection ratio (FIR) and polymer injection ratio (PIR), which are the injection parameters of the foam and the polymer, respectively. In addition, the workability of the conditioned soil was evaluated through the slump test. The peak and yield stresses of the conditioned soil with respect to the injection parameters were evaluated through the rheogram, which was derived from the measured torque data in the pressurized vane test. As FIR increased or PIR decreased, the workability of the conditioned soil increased, and the maximum torque, peak stress, and yield stress decreased. The peak stress and yield stress of the specimen from the laboratory pressurized vane test correspond to the workability evaluated by the slump tests, which implies the applicability of the proposed test for evaluating the rheological properties of excavated soil.