디스크커터 개별 굴착 거리를 고려한 마모량 예측 머신러닝 모델
Application of machine learning model to predict individual disc cutter wear amount considering the cutter travel distance
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초록
쉴드 TBM을 활용한 터널 굴착 사례가 증가하면서, 공기 단축 및 경제성 향상을 위한 연구가 활발히 진행되고 있다. 정확한 디스크커터 교체(cutter head intervention, CHI) 주기 예측은 TBM 다운타임 감소와 디스크커터 사용 효율 증대를통해 TBM을 활용한 터널 공사의 경제성을 높일 수 있다. 특히, 계획된 교체 주기와 실제 교체 횟수 또는 수량 간의 차이를 최소화함으로써 예상보다 잦은 교체로 인한 다운타임 증가와 비용 상승을 방지하는 데 기여한다. 본 연구에서는 쉴드TBM 막장면에 설치된 각 커터의 굴착 이동 거리를 고려하여 디스크 커터 마모량을 예측하는 머신러닝 모델을 개발하였다. 머신러닝 모델의 디스크커터 마모량 예측 정확도를 검증하기 위하여 국내 터널현장 경암지반 구간에서 수집된 TBM운영데이터 및 CHI 기록을 활용하였으며, 디스크커터의 마모를 유발하는 9가지 영향인자의 마모 영향도를 평가하였다. 본 연구에서 제시된 디스크커터 마모 예측 모델은 교체 주기의 정확도를 높여 기존 예측 방법론을 개선하고, 과도한 교체로 인한 다운타임과 비용 상승을 줄여 국내 쉴드 TBM의 공사비용 저감에 기여할 것으로 기대된다.
Abstract
Replacing damaged or worn disc cutters in tunnel boring machines (TBMs) incurs significant expenses and operational downtime, which directly impact TBM utilization and advancement rates. Therefore, accurately predicting the lifespan of disc cutters is essential for optimizing TBM efficiency in tunneling projects. This research introduces a new predictive model based on machine learning, specifically designed to forecast disc cutter wear by considering the travel distances of individual cutters. The model utilizes data from the cutter head intervention (CHI) report of the hard rock excavation of the tunneling project in Korea. The developed model achieved high accuracy in predicting the excavation lengths achievable with a single set of cutters by considering 9 factors that induce the cutter wear. This novel approach is poised to enhance current methodologies for predicting disc cutter lifespan, thereby potentially reducing costs and minimizing operational disruptions associated with cutter replacement.
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