All Issue

2025 Vol.27, Issue 3 Preview Page

Research Paper

Application of data augmentation for enhancing machine learning-based TBM disc cutter wear prediction

데이터 증강 기법을 적용한 개선된 머신러닝 기반 TBM 디스크 커터 마모 예측

Yuri Yeom1
,
Hangseok Choi2
,
Yerim Yang3
,
Kibeom Kwon4*
염 유리1
,
최 항석2
,
양 예림3
,
권 기범4*
1Master Student, Dept. of Civil, Environmental and Architectural Engineering, Korea University
2Professor, School of Civil, Environmental and Architectural Engineering, Korea University
3Ph.D. Student, Dept. of Civil, Environmental and Architectural Engineering, Korea University
4Researcher, Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University
1정회원, 고려대학교 건축사회환경공학과 석사과정
2정회원, 고려대학교 건축사회환경공학부 교수
3정회원, 고려대학교 건축사회환경공학과 박사과정
4정회원, 고려대학교 미래건설환경융합연구소 연구원
*Corresponding Author
31 May 2025. pp. 217-232
PDF XML Citation
Abstract

Excessive wear on disc cutters during tunnel boring machine (TBM) construction reduces excavation efficiency, highlighting the need for timely detection. While previous studies have explored the prediction of disc cutter wear, they often struggle to effectively detect excessive wear due to its infrequent occurrence. This study incorporated data augmentation and machine learning techniques for predicting disc cutter wear and evaluating the effectiveness of data augmentation in improving excessive wear detection. Initially, the wear amount for each ring excavation was categorized into two classes based on statistical analysis: safe (<0.337 mm) and warning (≥0.337 mm). To address the data imbalance between these classes, synthetic minority oversampling technique (SMOTE) was applied. Subsequently, classification models were developed using the augmented dataset and machine learning techniques, specifically Random Forest and eXtreme Gradient Boosting. The results demonstrated that data augmentation enhanced both accuracy and F1 score, while reducing the performance gap between the two wear classes. This is attributed to the enhanced learning of the excessive wear mechanism, achieved through the augmentation of excessive wear data. Feature importance analysis revealed that cutter revolution distance exhibited the strongest correlation with cutter wear. This finding highlighted designing relatively narrower cutter spacing on the outer section of the cutterhead.

Keywords
TBM
Disc cutter
Wear
Data augmentation
Machine learning

TBM (tunnel boring machine) 시공 중 디스크 커터의 과다 마모는 굴진 성능 저하를 유발할 수 있어, 이를 적시에 탐지하는 것이 중요하다. 디스크 커터 마모량 예측에 대한 다양한 연구가 수행되었으나, 과다 마모 탐지 성능 향상에 중점을 둔 연구는 부족한 실정이다. 본 연구에서는 데이터 증강 및 머신러닝 기반 디스크 커터 마모 등급 예측 모델을 구축하고, 데이터 증강 기법 적용에 따른 과다 마모 탐지 개선 효과를 분석하였다. 먼저, 토압식 쉴드 TBM 현장의 디스크 커터 교체 이력을 바탕으로 통계 분석을 수행하여, 링 당 마모량을 양호 등급(0.337 mm 미만)과 경고 등급(0.337 mm 이상)을 구분하였다. 두 가지 등급 간 데이터 불균형 문제를 해결하기 위해 SMOTE (synthetic minority oversampling technique)를 적용하였으며, 증강 데이터와 머신러닝 기법(Random Forest, eXtreme Gradient Boosting)을 활용하여 예측 모델을 개발하였다. 데이터 증강 적용 결과, 정확도와 F1 score가 향상되었고 양호 등급-경고 등급 간 예측 성능 불균형 수준이 감소하였다. 이는 과다 마모 데이터의 증강이 과다 마모 발생 메커니즘 학습에 효과적으로 기여한 결과로 판단된다. 특성 중요도 분석을 통해 커터 회전 거리 커터 마모 예측에 가장 영향도가 높음을 확인하였고, 이는 커터헤드 외측에 상대적으로 좁은 커터 배치 간격 설계의 필요성을 보여준다.

키워드
TBM
디스크 커터
마모
데이터 증강
머신러닝
References
1

Akhlaghi, M.A., Bagherpour, R., Hoseinie, S.H. (2024), "Real-Time prediction of disc cutter wear in low-abrasive rocks: Integrating physico-mechanical properties and signal processing features through machine learning methods", Arabian Journal for Science and Engineering, pp. 1-25.

10.1007/s13369-024-09321-x
2

Bae, G.J., Choi, S.W., Chang, S.H., Lee, G.P., Song, B.C., Kim, K.B. (2015), "Analysis of inner parts in the disc cutters applied to the field tests", Journal of Korean Tunnelling and Underground Space Association, Vol. 17, No. 4, pp. 473-485.

10.9711/KTAJ.2015.17.4.473
3

Breiman, L. (2001), "Random forests", Machine Learning, Vol. 45, pp. 5-32.

10.1023/A:1010933404324
4

Bruland, A. (2000), Hard rock tunnel boring, Doctoral Thesis, Norwegian University of Science and Technology, pp. 14-31.

5

Chang, S.H., Choi, S.W., Park, Y.T., Lee, G.P., Bae, G.J. (2012), "Characterization of the deformation of a disc cutter in linear rock cutting test", Journal of Korean Tunnelling and Underground Space Association, Vol. 14, No. 3, pp. 197-213.

10.9711/KTAJ.2012.14.3.197
6

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P. (2002), "SMOTE: Synthetic minority over-sampling technique", Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357.

10.1613/jair.953
7

Chen, K., Jiao, S., Wang, J. (2023), TBM Design and Construction, Springer, Singapore, pp. 1-45.

10.1007/978-981-99-0059-6_1
8

Chen, T., Guestrin, C. (2016), "XGBoost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 785-794.

10.1145/2939672.2939785
9

Frenzel, C., Käsling, H., Thuro, K. (2008), "Factors influencing disc cutter wear", Geomechanik und Tunnelbau, Vol. 1, No. 1, pp. 55-60.

10.1002/geot.200800006
10

Gehring, K. (1995), "Prognosis of advance rates and wear for underground mechanized excavations", Felsbau, Vol. 13, No. 6, pp. 439-448.

11

Gong, Q., Yin, L., Ma, H., Zhao, J. (2016), "TBM tunnelling under adverse geological conditions: An overview", Tunnelling and Underground Space Technology, Vol. 57, pp. 4-17.

10.1016/j.tust.2016.04.002
12

Hall, M.A. (1999), Correlation-based feature selection for machine learning, Doctoral Thesis, The University of Waikato, Hamilton, New Zealand, pp. 51-74.

13

Hassanpour, J., Rostami, J., Tarigh Azali, S., Zhao, J. (2014), "Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel, Iran", Tunnelling and Underground Space Technology, Vol. 43, pp. 222-231.

10.1016/j.tust.2014.05.007
14

Hauke, J., Kossowski, T. (2011), "Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data", Quaestiones Geographicae, Vol. 30, No. 2, pp. 87-93.

10.2478/v10117-011-0021-1
15

Jamshidi, M., Rimpault, X., Balazinski, M., Chatelain, J.F. (2020), "Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining", The International Journal of Advanced Manufacturing Technology, Vol. 106, pp. 3859-3868.

10.1007/s00170-019-04880-y
16

Kang, T.H., Choi, S.W., Lee, C., Chang, S.H. (2022), "A study on the prediction of disc cutter wear using TBM data and machine learning algorithm", Tunnel and Underground Space, Vol. 32, No. 6, pp. 502-517.

10.7474/TUS.2022.32.6.502
17

Kilic, K., Toriya, H., Kosugi, Y., Adachi, T., Kawamura, Y. (2022), "One-dimensional convolutional neural network for pipe jacking EPB TBM cutter wear prediction", Applied Sciences, Vol. 12, No. 5, 2410.

10.3390/app12052410
18

Kim, D., Shin, Y.J., Kwon, K., Lee, C., Kim, D., Choi, H. (2024), "Application of machine learning model to predict individual disc cutter wear amount considering the cutter travel distance", Journal of Korean Tunnelling and Underground Space Association, Vol. 26, No. 6, pp. 611-622.

10.9711/KTAJ.2024.26.6.611
19

Kwon, K., Choi, H., Jung, J., Kim, D., Shin, Y.J. (2025), "Prediction of abnormal TBM disc cutter wear in mixed ground condition using interpretable machine learning with data augmentation", Journal of Rock Mechanics and Geotechnical Engineering, Vol. 17, No. 4, pp. 2059-2071.

10.1016/j.jrmge.2024.05.027
20

Kwon, K., Hwang, B., Park, H., Oh, J.Y., Choi, H. (2024), "Enhancing machine learning-based anomaly detection for TBM penetration rate with imbalanced data manipulation", Journal of Korean Tunnelling and Underground Space Association, Vol. 26, No. 5, pp. 519-532.

10.9711/KTAJ.2024.26.5.519
21

Liu, Q., Liu, J., Pan, Y., Zhang, X., Peng, X., Gong, Q., Du, L. (2017), "A wear rule and cutter life prediction model of a 20-in. TBM cutter for granite: A case study of a water conveyance tunnel in China", Rock Mechanics and Rock Engineering, Vol. 50, 1303-1320.

10.1007/s00603-017-1176-4
22

Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Ali, H.F.H., Hasan, A.M., Khishe, M., Mahmud, H. (2021), "Machine learning forecasting models of disc cutters life of tunnel boring machine", Automation in Construction, Vol. 128, 103779.

10.1016/j.autcon.2021.103779
23

Ren, D.J., Shen, S.L., Arulrajah, A., Cheng, W.C. (2018), "Prediction model of TBM disc cutter wear during tunnelling in heterogeneous ground", Rock Mechanics and Rock Engineering, Vol. 51, pp. 3599-3611.

10.1007/s00603-018-1549-3
24

Rostami, J. (1997), Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure, Doctoral Thesis, Colorado School of Mines, Golden, Colorado, USA, pp. 55-125.

25

Shen, X., Chen, X., Fu, Y., Cao, C., Yuan, D., Li, X., Xiao, Y. (2022), "Prediction and analysis of slurry shield TBM disc cutter wear and its application in cutter change time", Wear, Vol. 498-499, 204314.

10.1016/j.wear.2022.204314
26

Shin, Y.J., Kwon, K., Bae, A., Choi, H., Kim, D. (2024), "Machine learning-based prediction model for disc cutter life in TBM excavation through hard rock formations", Tunnelling and Underground Space Technology, Vol. 150, 105826.

10.1016/j.tust.2024.105826
27

Su, W., Li, X., Jin, D., Yang, Y., Qin, R., Wang, X. (2020), "Analysis and prediction of TBM disc cutter wear when tunneling in hard rock strata: A case study of a metro tunnel excavation in Shenzhen, China", Wear, Vol. 446-447, 203190.

10.1016/j.wear.2020.203190
28

Wang, L., Li, H., Zhao, X., Zhang, Q. (2017), "Development of a prediction model for the wear evolution of disc cutters on rock TBM cutterhead", Tunnelling and Underground Space Technology, Vol. 67, pp. 147-157.

10.1016/j.tust.2017.05.003
29

Yu, H., Tao, J., Huang, S., Qin, C., Xiao, D., Liu, C. (2021), "A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead", Automation in Construction, Vol. 124, 103603.

10.1016/j.autcon.2021.103603
30

Yu, L., Liu, H. (2003), "Feature selection for high-dimensional data: A fast correlation-based filter solution", Proceedings of the 20th International Conference on Machine Learning, Washington DC, USA, pp. 856-863.

31

Zhang, N., Shen, S.L., Zhou, A. (2023), "A new index for cutter life evaluation and ensemble model for prediction of cutter wear", Tunnelling and Underground Space Technology, Vol. 131, 104830.

10.1016/j.tust.2022.104830
32

Zhao, Y., Yang, H., Chen, Z., Chen, X., Huang, L., Liu, S. (2019), "Effects of jointed rock mass and mixed ground conditions on the cutting efficiency and cutter wear of tunnel boring machine", Rock Mechanics and Rock Engineering, Vol. 52, pp. 1303-1313.

10.1007/s00603-018-1667-y
Information
  • Publisher :Korean Tunneling and Underground Space Association
  • Publisher(Ko) :한국터널지하공간학회
  • Journal Title :Journal of Korean Tunnelling and Underground Space Association
  • Journal Title(Ko) :한국터널지하공간학회 논문집
  • Volume : 27
  • No :3
  • Pages :217-232
  • Received Date : 2025-04-04
  • Revised Date : 2025-05-12
  • Accepted Date : 2025-05-22
  • DOI :https://doi.org/10.9711/KTAJ.2025.27.3.217
Journal Informaiton Journal of Korean Tunnelling and Underground Space Association Journal of Korean Tunnelling and Underground Space Association
Online Submission & Review System
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close