• 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.16 no.6
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• pp.533-542
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• 2014

In this study TBM disc cutter prediction models including Gehring, CSM and NTNU models were investigated and the characteristics of the models were examined. The influence of penetration, uniaxial compressive strength and abrasiveness index on the models was analyzed. The life of disc cutter linearly increases with penetration per revolution and decreases with increasing uniaxial compressive strength of rocks. As the abrasiveness index, CAI, increases, the life of disc cutter in Gehring and CSM model decreases. On the contrary, the life of disc cutter life in NTNU model decreases with increasing CLI. Also, comparisons of predicted disc life were made between models using actual job site data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.3
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• pp.475-488
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• 2017

Wear prediction of TBM disc cutters is a very important issue for hard rock TBMs as number of cutter head intervention. In this regard, some model such as NTNU, Gehring model, CSM models have been used to predict disc cutter wear and intervention interval. There are some deficiencies in these models. This paper developed a new test method for wear prediction for TBM disc cutter and proposed a new abrasion index. In this regard, different abrasivity indices along with their testing methods are explained. A comparative study is performed to develop the predictability of different cutter life evaluation methods and index. The evaluation of the new methods proposed in this paper shows a very good agreement with the actual cutter life and intervention interval length. The proposed tester and index can be easily used to predict the intervention interval length and cutter wear evaluation in both planning and construction stages of a TBM tunneling project.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1091-1104
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• 2018

Wear prediction of TBM disc cutters is a very important issue during design as well as construction stages for hard rock TBMs as the cutter head intervention is directly related to the time and cost of tunneling. For that, some methods such as NTNU, CSM and Gehring models were used to predict disc cutter wear and intervention interval. There are however some problems to be addressed in these models in terms of accuracy and time for testing, so that a NAT (New Abrasion Tester) model has been developed in order to achieve simplicity and reliability together at the same time (Farrokh and Kim, 2018). On the basis, the proposed NAT model has been applied to ${\bigcirc}{\bigcirc}$ project in Korea. A comparative study was performed to compare with the conventional methods and as a result the NAT model showed a very good agreement with actual cutter life. The NAT model will be further applied to other projects to establish credibility.

• Journal of the Korean Society of Propulsion Engineers
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• v.18 no.4
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• pp.67-73
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• 2014

The dry sliding wear behavior of graphite that is used as the sealing material to cut off hot gas was evaluated as a function of applied load, sliding speed and temperature. The reciprocating wear tests were carried out at room temperature and elevated temperatures. An attempt has been made to develop a mathematical model by response surface methodology and an analysis of variance technique was applied to confirm the validity of the developed model. Also, the wear mechanism was compared through the observation of the worn surface by SEM analysis.

• 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.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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.2
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• pp.237-248
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• 2014

Linear cutting test is known to be very effective to determine machine parameters (i.e. thrust force and torque) and to estimate penetration rate of TBM and other operation conditions. Although the linear cutting test has significant advantages, the test is expensive and time-consuming because it requires large size specimen and high load capacity of the testing machine. Therefore, a few empirical prediction models (e.g. CSM, NTNU and QTBM) alternatively adopt laboratory index tests to estimate design parameters of TBM. This study discusses the estimation method of TBM machine parameters and disc cutter consumption using punch penetration test and Cerchar abrasion test of which the researches are rare. The cutter forces and cutter consumption can be estimated by the empirical models derived from the relationship between laboratory test result with field data and linear cutting test data. In addition, the estimation process was programmed through which the design parameters of TBM (e.g. thrust, torque, penetration rate, and cutter consumption) are automatically estimated using laboratory test results.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.553-581
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• 2022

Shortly after tunnel boring machine (TBM) was introduced in the tunneling industry, the use of TBM has surprisingly increased worldwide due to its performance together with the benefit of being safely and environmentally friendly. One of the main cost items in the TBM tunneling in rock and soil is changing damaged or worn cutters. It is because that the cutter change is a time-consuming and costly activity that can significantly reduce the TBM utilization and advance rate and has a major effect on the total time and cost of TBM tunneling projects. Therefore, the importance of accurately evaluating the cutter life can never be overemphasized. However, the prediction of cutter wear in soil, rock including mixed face is very complex and not yet fully clarified, subsequently keeping engineers busy around the world. Various prediction models for cutter wear have been developed and introduced, but these models almost usually produce highly variable results due to inherent uncertainties in the models. In this study, a case study of design and construction of disc cutter change is introduced and analyzed, rather than proposing a prediction model of cutter wear. As the disc cutter is strongly affected by the geological condition, TBM machine characteristic and operation, authors believe it is very hard to suggest a generalized prediction model given the uncertainties and limitations therefore it would be more practical to analyze a real case and provide a detailed discussion of the difference between prediction and result for the cutter change. By doing so, up-to-date idea about planning and execution of cutter change in practice can be promoted.