• Journal of the Korean GEO-environmental Society
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• v.8 no.6
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• pp.29-36
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• 2007

The control and prediction of surface settlement, gradient and ground displacement are the main factors in urban tunnel construction. This paper carried out the estimation and prediction of ground behavior around tunnel due to excavation using computational method and case study in detail for the analysis of deformation behavior in urban NATM tunnel. Computational method was performed by FLAC-2D with strain softening model and elastic plastic model. Field measurements of surface subsidence and ground displacement were adopted to monitor the ground behavior resulting from the tunneling and these values were applied to modify tunnel design parameters on construction.

• 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.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Geomechanics and Engineering
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• v.30 no.4
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• pp.325-336
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• 2022

This paper analyzes the ground surface settlements induced by side-by-side twin shield tunnels bored in sedimentary soils, which primarily consist of sand with clay strata above the tunnel crown. The measurements were obtained during the construction of twin tunnels underneath the Incheon International Airport (IIA) located in Korea. The measured surface settlement troughs are approximated with Gaussian functions. The trough width parameters i and K of the settlement troughs produced by the first and second tunnel passings are determined, along with those for the total settlement trough. The surface settlement troughs produced by the first shield passing are reasonably represented by a symmetric Gaussian curve. The surface settlement troughs induced by the second shield tunnel display marginal asymmetric shapes at selected sections. The total settlement troughs are fitted both with a shifted symmetric Gaussian function and the superposition method utilizing an asymmetric function for the incremental trough produced by the second tunnel. It is revealed that the superposition method does not always produce better fits with the total settlement. Instead, the shifted symmetric Gaussian function is overall demonstrated to provide more favorable agreements with the recordings. Therefore, the shifted symmetric Gaussian function is recommended to be used in the design for the prediction of the settlement in clays caused by twin tunneling considering the simplicity of the procedure compared with the superposition method. The amount of increase in the width parameter K for the twin tunnel relative to that for the single tunnel is quantified, which can be used for a preliminary estimate of the surface settlement in clay induced by twin shield tunnels.

• Journal of the Korean Geotechnical Society
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• v.24 no.1
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• pp.61-70
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• 2008

The control and prediction of surface settlement, gradient and ground displacement are the main factors in shallow tunnel design and construction in urban area. For deformation analysis of shallow tunnel due to excavation it is important to identify possible deformation mechanism of shear bands developing from tunnel shoulder to the ground surface. This paper investigaties quantitatively the deformation behavior of shallow tunneling by model tunnel test and strain softening analysis Incorporating the reduction of shear stiffness and strength parameters. The comparison of model tunnel test result and numerical simulation using strain softening analysis showed good agreement in crown settlement, normalized subsidence settlement and developing shear bands above tunnel shoulder. In this study, it is blown that the strain softening modeling is applicable to the nonlinear deformation analysis of shallow tunnel.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.453-467
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• 2018

The excavation performance and the cutting tool wear prediction of shield TBM are very important issues for design and construction in TBM tunneling. For hard-rock TBMs, CSM and NTNU model have been widely used for prediction of disc cutter wear and penetration rate. But in case of soft-ground TBMs, the wear evaluation and the excavation performance have not been studied in details due to the complexity of the ground behavior and therefore few testing methods have been proposed. In this study, a new soil abrasion and penetration tester (SAPT) that simulates EPB TBM excavation process is introduced which overcomes the drawbacks of the previously developed soil abrasivity testers. Parametric tests for penetration rate, foam mixing ratio, foam concentration were conducted to evaluate influential parameters affecting TBM excavation and also ripper wear was measured in laboratory. The results of artificial soil specimen composed of 70% illite and 30% silica sand showed TBM additives such as foam play a key role in terms of excavation and tool wear.

• 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.