• Title/Summary/Keyword: 커터 수명 예측

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A comparative study on the TBM disc cutter wear prediction model (TBM 디스크 커터 마모 예측 모델 비교 연구)

  • Ko, Tae Young;Yoon, Hyun Jin;Son, Young Jin
    • 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.

Case study of design and construction for cutter change in EPB TBM tunneling (EPB 쉴드 TBM 커터 교체 설계 및 시공 사례 분석)

  • Lee, Jae-won;Kang, Sung-wook;Jung, Jae-hoon;Kang, Han-byul;Shin, Young Jin
    • 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.

Application of Multiple Linear Regression Analysis and Tree-Based Machine Learning Techniques for Cutter Life Index(CLI) Prediction (커터수명지수 예측을 위한 다중선형회귀분석과 트리 기반 머신러닝 기법 적용)

  • Ju-Pyo Hong;Tae Young Ko
    • 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.

Estimation of Cerchar abrasivity index based on rock strength and petrological characteristics using linear regression and machine learning (선형회귀분석과 머신러닝을 이용한 암석의 강도 및 암석학적 특징 기반 세르샤 마모지수 추정)

  • Ju-Pyo Hong;Yun Seong Kang;Tae Young Ko
    • 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.

Development of a new test method for the prediction of TBM disc cutters life (TBM 디스크 커터의 수명 예측 방법 개발)

  • Kim, Dae-Young;Farrokh, Ebrahim;Jung, Jae-Hoon;Lee, Jae-Won;Jee, Sung-Hyun
    • 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.

Statistical analysis of NTNU test results to predict rock TBM performance (TBM 굴진성능 예측을 위한 NTNU 시험결과의 분석)

  • Choi, Soon-Wook;Chang, Soo-Ho;Lee, Gyu-Phil;Bae, Gyu-Jin
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.13 no.3
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    • pp.243-260
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    • 2011
  • To predict TBM performance in design stage is indispensable for its successful application. The NTNU model, one of the representative TBM performance prediction models uses two distinct parameters such as DRI and CLI obtained from three different tests on bored rock cores. Based on DRI and CLI, it is possible to predict TBM advance rate and cutter life in the NTNU model. In this study, NTNU testing methods and their related testing equipments were introduced to measure DRl and CLI for the NTNU model. Then, in order to derive their relationships, the two key parameters measured for 39 domestic rocks were compared with physico-mechanical properties of rock such as uniaxial compressive strength and quartz content. Lastly, the experimental results were also compared with NTNU database to verify their reliability.

Estimation of design parameters of TBM using punch penetration and Cerchar abrasiveness test (압입시험 및 세르샤 마모시험에 의한 TBM의 설계변수 추정)

  • Jeong, Ho-Young;Lee, Sudeuk;Jeon, Seokwon
    • 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.