- 권한신청
- P-ISSN2233-8292
- E-ISSN2287-4747
- KCI
ISSN : 2233-8292
토사터널의 쉴드 TBM 데이터 시계열 분석을 통한 막장 전방 예측 연구
A ground condition prediction ahead of tunnel face utilizing time series analysis of shield TBM data in soil tunnel
김병규 (고려대학교)
정희영 (고려대학교)
김해만 (고려대학교)
이인모 (고려대학교)
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초록
토압식(Earth Pressure-Balanced, EPB) 쉴드 TBM 기계데이터 분석을 통해 토사터널의 특징이 반영된 막장 전방 예측방법을 제안하였다. 기존에 암반과 토사가 혼합된 복합 지반의 예측에 적용하였던 시계열 분석 모델을 토사터널에 적용가능하도록 수정하였다. 또한 수정된 모델을 사용하여, 토사 종류에 따라 쏘일 컨디셔닝 재료를 선택하는 것이 타당한지연구하였다. 이를 위해 Self-Organizing Map (SOM) 군집화(clustering) 분석을 수행하였다. 그 결과 무엇보다도 지반타입이 #200체 통과량 35% 기준으로 분류되어야 한다는 것을 확인하였다. 또한 TBM 기계데이터 분석을 통해 수정된모델이 지반 타입을 예측하는데 사용될 수 있음을 확인하였다. 수정된 기준에 따라 지반 타입을 분류하고 시계열 분석을수행하면, 10막장 전방 지반에 대해서 98%의 높은 예측 정확도를 보였으며, 이를 통해 수정된 방법의 우수성이 입증되었다. 특히 지반 타입 변화 구간에 대한 예측 정확도도 약 93%로, 10막장 전방에서 지반 타입 변화 여부를 미리 확인할수 있게 되었다
- keywords
- 인공신경망, ARIMA 모델, 시간지연신경망, 막장 전방 예측, 토압식 쉴드 TBM, Artificial neural network (ANN), Autoregressive integrated moving average (ARIMA) model, Time delay neural network (TDNN), Ground condition prediction, Earth pressure-balanced (EPB) shield TBM
Abstract
This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.
- keywords
- 인공신경망, ARIMA 모델, 시간지연신경망, 막장 전방 예측, 토압식 쉴드 TBM, Artificial neural network (ANN), Autoregressive integrated moving average (ARIMA) model, Time delay neural network (TDNN), Ground condition prediction, Earth pressure-balanced (EPB) shield TBM
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