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Study on the effective parameters and a prediction model of the shield TBM performance

쉴드 TBM 굴진 주요 영향인자분석 및 굴진율 예측모델 제시

  • Jo, Seon-Ah (Structural & Seismic Tech. Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute) ;
  • Kim, Kyoung-Yul (Structural & Seismic Tech. Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute) ;
  • Ryu, Hee-Hwan (Structural & Seismic Tech. Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute) ;
  • Cho, Gye-Chun (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • 조선아 (한국전력공사 전력연구원 차세대송변전연구소 구조내진연구실) ;
  • 김경열 (한국전력공사 전력연구원 차세대송변전연구소 구조내진연구실) ;
  • 류희환 (한국전력공사 전력연구원 차세대송변전연구소 구조내진연구실) ;
  • 조계춘 (한국과학기술원 건설 및 환경공학과)
  • Received : 2019.03.08
  • Accepted : 2019.04.16
  • Published : 2019.05.31

Abstract

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

도심지 터널 공사가 많아지면서 이에 따른 소음, 진동, 교통불편 및 민원 저감을 위해 TBM 굴착이 증가하고 있다. 그러나 이러한 추세에도 불구하고 국내 TBM 공동구 설계 및 시공을 위한 기준들은 대부분 해외기술(일본, 독일 등)을 이용하고 있어 국내환경을 고려하지 못하고 있다. 특히, 공동구 TBM 설계의 주요 기준이 되는 굴진율은 대부분 일축압축강도만으로 산정되며 이마저도 실제 현장 특성과 맞지 않아 개선이 필요하다. 본 연구에서는 국내 현장에 적합한 굴진율을 예측하기 위해 수행되었다. 이를 위해 시공 중인 소단면 쉴드 TBM 굴착 현장의 지반 및 굴진데이터를 수집하고 상관관계 분석을 통해 굴진율에 영향을 미치는 주요인자를 파악하였다. 도출된 영향인자들은 통계적 분석기법을 기반으로 한 다중선형 회귀분석에 적용되어 굴진율을 예측하는 회귀식의 예측변수로 이용되었다. 결과적으로 회귀분석을 통해 도출된 회귀식은 일축압축강도와 절리간격을 예측변수로 추정되었으며, 해외 경험식과 비교하여 국내현장 굴진율의 예측 정확도가 높은 것으로 나타났다. 다만, 이 회귀식을 타 국내 현장에 적용할 경우 예측오차가 다소 증가하였다. 회귀식이 갖는 이와 같은 적용 한계를 개선하기 위해서는 추가적인 연구를 통해 현장조건에 제약을 받지 않는 굴진율 예측모델 도출이 필요할 것으로 보인다.

Keywords

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Fig. 1. Geological strata of the TBM excavation site

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Fig. 2. Variation of the penetration rate with the geological strata

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Fig. 3. Comparison between measured and calculated penetration rate

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Fig. 4. Comparison between measured and calculated penetration rate correlation between the penetration depth and intact rock properties

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Fig. 5. Correlation of the penetration depth with rock mass properties

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Fig. 6. Correlation of the penetration rate with performance parameters

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Fig. 7. Correlation of the thrust force with the geotechnical parameters such as joint spacing, RQD and RMR

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Fig. 8. Comparison of the linear relation between predicted and measured penetration rate

Table 1. Review of various performance prediction models

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Table 2. Properties of the rock located in the site passing through the tunnel

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Table 3. Specification of EPB shield TBM

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Table 4. List of a database for statistical analysis

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Table 5. Empirical models for predicting TBM performance based on UCS

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Table 6. Prediction models estimated by regression analysis

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