• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.575-589
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• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.641-656
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• 2019

In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.5
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• pp.483-496
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• 2013

This paper presents the shield TBM technology in soft ground tunnelling. In order to perform this study, a scale model test was carried out using the developed small scaled shield TBM machine. The various instrumentations were conducted during the simulation of tunnelling. In addition, the ground behavior due to the shield TBM operation parameters was measured during the simulation. Based on the simulation results, the stability of the ground was evaluated and the fundamental shield TBM tunnelling technique in the soft ground was suggested. In conclusion, design's reliability through laboratory small scale model test about Shield-TBM section was obtained, and both the improvement plan for safety during construction and the construction plan for securing airport runway's safety during tunnel passing by Shield-TBM propulsion were suggested.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.273-280
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• 2003

This paper describes a study of the effect of shield tunnel construction on the liners of nearby existing perpendicular tunnels. The research programme investigated the influence of tunnel proximity and alignment, liner stiffness on the nature of the interactions between closely spaced tunnels in clay. A total of two sets of carefully controlled 1g physical model tests, including the same test for repeatability, were performed. A cylindrical test tank was developed and used to produce clay samples of Speswhite kaolin. In each of the tests, three model tunnels were installed in order to conduct two interaction experiments in one clay sample. The tunnel liners were installed using a model tunnelling machine that was designed and developed to simulate the construction of a full scale shield tunnel. The first tunnel liner was instrumented to investigate its behaviour due to the installation of each of the new tunnels. The interaction mechanisms observed from the physical model tests are discussed and interpreted.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.479-489
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• 2018

In this paper, a comparative study of the effects of soil modelling on the interaction between tunnelling in soft soil and adjacent piled structure is presented. Several three-dimensional finite element analyses are performed to study the deformation of pile caps and piles as well as tunnel internal forces during the construction of an underground tunnel. The soil is modelled by two material models: the simple, yet approximate Mohr Coulomb (MC) yield criterion; and the complex, but reasonable hardening soil (HS) model with hyperbolic relation between stress and strain. For the former model, two different values of the soil stiffness modulus ($E_{50}$ or $E_{ur}$) as well as two profiles of stiffness variation with depth (constant and linearly increasing) were used in attempts to improve its prediction. As these four attempts did not succeed, a hybrid representation in which the hardening soil is used for soil located at the highly-strained zones while the Mohr Coulomb model is utilized elsewhere was investigated. This hybrid representation, which is a compromise between rigorous and simple solutions yielded results that compare well with those of the hardening soil model. The compared results include pile cap movements, pile deformation, and tunnel internal forces. Problem symmetry is utilized and, therefore, one symmetric half of the soil medium, the tunnel boring machine, the face pressure, the final tunnel lining, the pile caps, and the piles are modelled in several construction phases.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.507-520
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• 2014

In this study, the surface displacement was investigated according to the various depth of cover when the tunnel excavation equipment was used in a clay soil. For this the laboratory scaled model test was carried out using the soil sample similar to the in-situ conditions. We carried out four tests according to tunnel depth(1.5D, 2.0D, 2.5D, 3.0D). The distribution of impact due to tunnelling was quantitatively analyzed in the three-dimension by measuring the surface displacement. In addition, the pattern of surface displacements was figured out.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.3
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• pp.221-243
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• 2023

The use of roadheaders has been increasing to mitigate the problems of noise and vibration during tunneling operations in urban area. Since lack of experience of roadheader for hard rock, the selection of appropriate machines and the evaluation of cutting rates have been challenging. Currently, empirical models developed overseas are commonly used to evaluate cutting rates, but their effectiveness has not been verified for domestic rocks. In this paper, a comprehensive literature review was conducted to assess the rock cutting force, cutterhead capacity, and cutting rate to select the appropriate machine and evaluate its performance. The cutterhead capacity was reviewed based on the literature results for the site. Furthermore, a new empirical model and simplified method for predicting cutting rates were proposed through data analysis in relation to operation time and rock strength, and compared with those of the conventional model from the manufacturer. The results show good agreement for high strength range upper 80 MPa of uniaxial compressive strength.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.305-317
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• 2015

This paper is to perform 3-dimensional numerical analysis considering face pressure, backfill pressure, excavation length, soil model and element size for selecting key factors of ground surface settlement due to shield TBM advancement. According to the numerical analysis results, backfill pressure and soil model are governing factors inducing ground surface settlement. To complement this study, the ground conditions and characteristics of the boring machine will be considered using numerical analysis.