• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.157-164
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• 2002

Based on the results of prototype air-shaft construction, penetration capacity of RBM(Raise Boring Machine) was analyzed and compared with TBM(Tunnel Boring Machine) performance in this study. Utilization, down time, net penetration rate and advance rate were evaluated and compared. By conducting the laboratory tests for rock properties with the analysis of penetration capacity, relation of penetration capacity and geotechnical parameters was studied. The results showed that much more higher value of utilization, however lower value of net penetration rate for RBM was obtained compared to those of TBM. In addition, as the strength of rock penetrated increased, higher value of net penetration rate was obtained contrarily to the results of TBM performance. Finally, new relationship between total hardness and net penetration rate for weak and weathered rock was derived from these results.

• Computers and Concrete
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• v.22 no.2
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• pp.239-248
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• 2018

TBM penetration rate is a function of intact rock properties, rock mass conditions and TBM operational parameters. Machine rate of penetrationcan be predicted by knowledge of the ground conditions and its effects on machine performance. The variation of TBM operational parameters such as penetration rate and thrust plays an important role in its performance. This study presents the results of the analysis on the TBM penetration rates in schistose rock types present along the alignment of Golab tunnel based on the analysis of a TBM performance database established for every stroke through different schistose rock types. The results of the analysis are compared to the results of some empirical and theoretical predictive models such as NTH and QTBM. Additional analysis was performed to find the optimum thrust and revolution per minute values for different schistose rock types.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.519-532
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• 2024

Anomaly detection for the penetration rate of tunnel boring machines (TBMs) is crucial for effective risk management in TBM tunnel projects. However, previous machine learning models for predicting the penetration rate have struggled with imbalanced data between normal and abnormal penetration rates. This study aims to enhance the performance of machine learning-based anomaly detection for the penetration rate by utilizing a data augmentation technique to address this data imbalance. Initially, six input features were selected through correlation analysis. The lowest and highest 10% of the penetration rates were designated as abnormal classes, while the remaining penetration rates were categorized as a normal class. Two prediction models were developed, each trained on an original training set and an oversampled training set constructed using SMOTE (synthetic minority oversampling technique): an XGB (extreme gradient boosting) model and an XGB-SMOTE model. The prediction results showed that the XGB model performed poorly for the abnormal classes, despite performing well for the normal class. In contrast, the XGB-SMOTE model consistently exhibited superior performance across all classes. These findings can be attributed to the data augmentation for the abnormal penetration rates using SMOTE, which enhances the model's ability to learn patterns between geological and operational factors that contribute to abnormal penetration rates. Consequently, this study demonstrates the effectiveness of employing data augmentation to manage imbalanced data in anomaly detection for TBM penetration rates.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2001.10a
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• pp.115-120
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• 2001

In No. 1 tunnel for Kwnagju urban subway construction, net penetration rate of the shield TBM was analyzed. This tunnel of 540 m length is located in soil layers at starting and in hard rocks such as amphibolite and granitic gneiss at ending with 84 m length. The net penetration rate was dropped down to 2∼11 cm/hr in rock while 50∼80 cm/hr in soil. Theoretical penetration rate is analyzed in conditions of machine and rock in order to compare the actual net penetration rate. The relationships between net penetration rate and thrust force is also investigated in this report.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.27-41
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• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.162-169
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• 2003

Excavation by TBM can be characterized by a rock-machine interaction during the cutting process on a small scale, but on a large scale the interaction between the rock mass and TBM becomes very significant. For the planning and evaluation of TBM tunnelling it needs to understand rock fracture mechanism by a cutter or cutters on a small scale, and to estimate penetration rate, advance rate and utilization on a large scale. In this study rock chipping mechanism due to cutter-penetration is analysed by numerical simulation, showing that rock chipping is mainly occurred by tensile failure. Also, through the analysis of factors that affect on TBM procedures in various assessment systems, it is determined that the key elements that should be considered in the planning and evaluation of TBM tunnelling are classified into rock properties, the geological structures and properties of rock mass, and the structural and functional specifications of the machine. The user-friendly assessment tool is developed, so that penetration rate, advance rate and TBM utilization are evaluated from various input data. The tool developed in this study can be applied to a practical TBM tunnelling by understanding TBM tunnelling procedures.

• Tunnel and Underground Space
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• v.13 no.3
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• pp.196-206
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• 2003

In order to investigate the workability of Raise Boring Machine(RBM) such as utilization, penetration rate and advance rate, a vertical shaft of 98 m in length and 3.05 m in diameter was constructed in the layer of conglomerate by using the RBM in this study. In addition, field data from tow different construction sites including water-pump power plant tunnel, roadway tunnel and mining tunnel by RBM were collected and analyzed. The results show that the average weekly bored length is 19.3 m and its average utilization is between 54.3 % and 75.1 % very higher than that of the TBM(Tunnel Boring Machine). It also turns out that the bit force increases linearly with respect to the increase of the RPM(revolution per minute) of RBM. However, the net penetration rate decreases with the increase of bit force, RPM of RBM and depth of shaft. The findings of this study can be used to provide the useful information for the design of shaft and the selection of RBM.

• Tunnel and Underground Space
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• v.12 no.2
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• pp.115-119
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• 2002

Four tunnels have been planned to operate a large diameter shielded TBM in Gwangju urban subway construction site. No.1 tunnel has completely been excavated for 13 months operating. Net penetration rate and its relations with thrust farce of the shielded TBM are analysis in this report. This shallow depth tunnel of 536m length is located in soil layers at launching and in hard rocks at ending with 84 m length. The weekly net penetration rates haute dropped down as low as 20∼110 mm/hr in rock while 400∼800 mm/hr in soil. The actual penetration rates we proved to be high as the theoretical penetration rate which is analysis in consideration of conditions of machine and rock. And net penetration rate is investigated to increase linearly thrust force.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.541-547
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• 2021

To assess the stability of a slope and the likelihood of its loss or collapse requires information about the ground, such as the composition of the stratum and its mechanical characteristics. This information is generally gathered through standard penetration testing (SPT) and cone penetration testing. SPT is not widely used due to problems with accessing slopes, most of which are steep and without ramps. A drop cone penetrometer, a portable device that can make up for these shortcomings, can be used in a limited way in some circumstances. Therefore, we developed a portable drilling machine and a small dynamic cone penetration test module that can easily access a slope site and perform SPT. The correlation of the developed system's results with those from SPT was analyzed. Analysis of the correlation between the energy shear rate passing to the load during the different test types established that the energy shear rate is reflected in the test result. The correlation between corrected dynamic cone penetration testing and corrected SPT was N_d' = 3.13 N'.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.