• Tunnel and Underground Space
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• v.22 no.4
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• pp.284-295
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• 2012

Abrasiveness of rock plays an important role on the wear of rock cutting tools. In this study, Cerchar abrasiveness tests were carried out to assess the abrasiveness of 19 different Korean rocks. Cerchar abrasiveness test is widely used to assess the abrasiveness of rock because of its simplicity and inexpensive cost. This study examines the relationship between Cerchar Abrasiveness Index (CAI) and mechanical properties (uniaxial compressive strength, Brazilian tensile strength, Young's modulus, Poisson's ratio, porosity, shore hardness of rock), and the effect of quartz content, equivalent quartz content, which was obtained from XRD analysis. As a result of test, CAI was more influenced by petrographical properties than by the bonding strength of the matrix material of rock. CAI prediction model which consisted of UCS and EQC was proposed. CAI decreased linearly with the hardness of the steel pin. Numerical analysis was performed using Autodyn-3D for simulating the Cerchar abrasiveness test. In the simulations, most of pin wear occurred during the initial scratching distance, and CAI increased with the increase of normal loading.

• 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.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.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.