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A ground condition prediction ahead of tunnel face utilizing time series analysis of shield TBM data in soil tunnel

토사터널의 쉴드 TBM 데이터 시계열 분석을 통한 막장 전방 예측 연구

  • Jung, Jee-Hee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Kim, Byung-Kyu (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Chung, Heeyoung (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Kim, Hae-Mahn (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Lee, In-Mo (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 정지희 (고려대학교 건축사회환경공학부) ;
  • 김병규 (고려대학교 건축사회환경공학부) ;
  • 정희영 (고려대학교 건축사회환경공학부) ;
  • 김해만 (고려대학교 건축사회환경공학부) ;
  • 이인모 (고려대학교 건축사회환경공학부)
  • Received : 2018.12.19
  • Accepted : 2019.01.24
  • Published : 2019.03.31

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.

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Fig. 1. Example of deep neural networks

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Fig. 2. A time-delay neural network for one-dimensional input/output signals (Hassoun, 1995)

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Fig. 3. A result of the SOM clustering

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Fig. 4. The profile of machine data groups

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Fig. 5. Comparison between the soil conditioning materials and the machine data

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Fig. 6. The distribution of the percentage passing through the #200 sieve along the entire route

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Fig. 7. Comparison between the soil conditioning materials and the ground types

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Fig. 8. Comparison between the soil conditioning materials and the normalized machine data

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Fig. 9. Flow chart to run the developed time delay neural network (TDNN) engine (Jung at al., 2018)

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Fig. 10. The location of sections at job site

Table 1. Conditions at a job site

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Table 2. Properties of soils

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Table 3. Classification of ground types

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Table 4. Analysis cases

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Table 5. TDNN learning method and final model selection criteria (Jung at al., 2018)

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Table 6. The results of ANN engine

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Table 7. The results of analysis cases

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Table 8. Decision criteria for ground type classification in the TDNN model (modified from Jung et al. (2018))

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Table 9. Comparison of predicted and actual ground types

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Table 10. Comparison of average prediction accuracy

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Table 11. Soil conditioning materials according to predicted ground types

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