Geomechanics and Engineering

  • 제29권3호
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  • Pages.249-258
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Shield TBM disc cutter replacement and wear rate prediction using machine learning techniques

  • Kim, Yunhee (Department of Civil and Environmental Engineering, Dongguk University) ;
  • Hong, Jiyeon (Department of Civil and Environmental Engineering, Dongguk University) ;
  • Shin, Jaewoo (Department of Civil and Environmental Engineering, Dongguk University) ;
  • Kim, Bumjoo (Department of Civil and Environmental Engineering, Dongguk University)
  • 투고 : 2022.01.27
  • 심사 : 2022.03.10
  • 발행 : 2022.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

키워드

과제정보

This research was supported by the Basic Science Research Program through the National research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A02085845).

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