Journal of Korean Tunnelling and Underground Space Association (한국터널지하공간학회 논문집)

Korean Tunnelling and Underground Space Association (한국터널지하공간학회)
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A study on EPB shield TBM face pressure prediction using machine learning algorithms

머신러닝 기법을 활용한 토압식 쉴드TBM 막장압 예측에 관한 연구

  • Kwon, Kibeom (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Choi, Hangseok (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Oh, Ju-Young (ENG Division, Samsung C&T Corporation) ;
  • Kim, Dongku (Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University)
  • 권기범 (고려대학교 건축사회환경공학부) ;
  • 최항석 (고려대학교 건축사회환경공학부) ;
  • 오주영 (삼성물산 건설부문 ENG실) ;
  • 김동구 (고려대학교 미래건설환경융합연구소)
  • Received : 2022.02.21
  • Accepted : 2022.03.17
  • Published : 2022.03.31
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Abstract

The adequate control of TBM face pressure is of vital importance to maintain face stability by preventing face collapse and surface settlement. An EPB shield TBM excavates the ground by applying face pressure with the excavated soil in the pressure chamber. One of the challenges during the EPB shield TBM operation is the control of face pressure due to difficulty in managing the excavated soil. In this study, the face pressure of an EPB shield TBM was predicted using the geological and operational data acquired from a domestic TBM tunnel site. Four machine learning algorithms: KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), and XGB (eXtreme Gradient Boosting) were applied to predict the face pressure. The model comparison results showed that the RF model yielded the lowest RMSE (Root Mean Square Error) value of 7.35 kPa. Therefore, the RF model was selected as the optimal machine learning algorithm. In addition, the feature importance of the RF model was analyzed to evaluate appropriately the influence of each feature on the face pressure. The water pressure indicated the highest influence, and the importance of the geological conditions was higher in general than that of the operation features in the considered site.

쉴드TBM (Tunnel Boring Machine) 터널 시공에 있어 막장압 관리는 막장면 붕괴, 지반침하 등을 방지하여 막장 안정성을 유지하는 데 중요한 역할을 담당한다. 특히, 챔버 내부의 굴착토로 막장압을 조절하는 토압식 쉴드TBM의 경우, 이수식 쉴드TBM에 비해 막장압의 관리가 어렵다. 본 연구에서는 국내 토압식 쉴드TBM 터널 시공 현장의 지반조건 및 굴진특성 데이터를 분석하여, 토압식 쉴드TBM 터널의 세그먼트 링별 막장압 예측모델을 제시하였다. 예측모델의 입력특성으로 7가지를 선정하였으며, 912개의 학습 데이터 세트(Training data set)와 228개의 시험 데이터 세트(Test data set)를 확보하였다. 최적의 토압식 쉴드TBM 막장압 예측모델 선정을 위하여 KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), XGB (eXtreme Gradient Boosting) 모델의 하이퍼파라미터(Hyperparameter)를 최적화하여 예측성능을 비교한 결과, RF 모델이 7.35 kPa의 평균 제곱근 오차(Root Mean Square Error, RMSE)로 가장 우수한 성능을 나타냈다. 추가적으로, RF 모델의 특성 중요도(Feature importance) 분석을 수행한 결과, 입력특성 중 수압의 영향도가 0.38로 가장 높았으며, 전반적으로 지반조건이 굴진특성보다 높은 중요도를 보여주었다.

Keywords

Acknowledgement

이 연구는 국토교통부/국토교통과학기술진흥원이 시행하고 한국도로공사가 총괄하는 "스마트건설기술개발국가R&D사업(과제번호 22SMIP-A158708-03)"의 지원으로 수행하였습니다.

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