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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

  • Kim, Yunhee (Dept. of Civil and Environmental Engineering, Dongguk University) ;
  • Hong, Jiyeon (Dept. of Civil and Environmental Engineering, Dongguk University) ;
  • Kim, Bumjoo (Dept. of Civil and Environmental Engineering, Dongguk University)
  • 김윤희 (동국대학교 건설환경공학과) ;
  • 홍지연 (동국대학교 건설환경공학과) ;
  • 김범주 (동국대학교 건설환경공학과)
  • Received : 2020.08.04
  • Accepted : 2020.08.21
  • Published : 2020.09.30

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.

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