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2020 Vol.22, Issue 5 Preview Page

Research Paper

Performance comparison of machine learning classification methods for decision of disc cutter replacement of shield TBM

쉴드 TBM 디스크 커터 교체 유무 판단을 위한 머신러닝 분류기법 성능 비교

Yunhee Kim1
,
Jiyeon Hong2
,
Bumjoo Kim3*
김 윤희1
,
홍 지연2
,
김 범주3*
1Ph.D. Student, Dept. of Civil and Environmental Engineering, Dongguk University
2Undergraduate Student, Dept. of Civil and Environmental Engineering, Dongguk University
3Professor, Dept. of Civil and Environmental Engineering, Dongguk University
1학생회원, 동국대학교 건설환경공학과 박사과정
2학생회원, 동국대학교 건설환경공학과 학부과정
3정회원, 동국대학교 건설환경공학과 교수
* Corresponding Author
30 September 2020. pp. 575-589
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Abstract

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.

Keywords
Shield TBM
Disc cutter
Machine learning
Classification method

최근 국내 터널에서 지속적으로 증가하고 있는 쉴드 TBM 공법의 주된 굴착도구는 디스크 커터로 굴진과정에서 자연스럽게 마모가 발생하고 이는 TBM의 굴진효능을 현저히 저하시키기 때문에 적절한 시기에 교체하는 것이 중요하다. 따라서 본 연구에서는 디스크 커터 교체 여부를 판단할 수 있는 예측 모델을 머신러닝 기법을 사용한 방법으로 제안하였다. 이를 위해 국내 기 시공된 쉴드 TBM 현장의 데이터 중 디스크 커터 소모에 상관성이 높은 굴진데이터(TBM 기계데이터, 지반정보 등)와 교체이력을 입력데이터로 사용하여 다양한 머신러닝 분류기법 중 서포트 벡터 머신, 최근접이웃 알고리즘, 의사결정트리 알고리즘을 사용하여 최적의 예측 모델을 구축하고 모델의 성능을 평가하기 위하여 분류성능평가 지표로 비교 분석하였다.

키워드
쉴드 TBM
디스크 커터
기계학습
분류기법
References
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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 : 22
  • No :5
  • Pages :575-589
  • Received Date : 2020-08-04
  • Revised Date : 2020-08-20
  • Accepted Date : 2020-08-21
  • DOI :https://doi.org/10.9711/KTAJ.2020.22.5.575
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