Geomechanics and Engineering

  • 제37권3호
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  • Pages.197-211
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  • 2024
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

EPB-TBM performance prediction using statistical and neural intelligence methods

  • Ghodrat Barzegari (Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz) ;
  • Esmaeil Sedghi (Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz) ;
  • Ata Allah Nadiri (Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz)
  • 투고 : 2022.07.06
  • 심사 : 2024.03.23
  • 발행 : 2024.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

This research studies the effect of geotechnical factors on EPB-TBM performance parameters. The modeling was performed using simple and multivariate linear regression methods, artificial neural networks (ANNs), and Sugeno fuzzy logic (SFL) algorithm. In ANN, 80% of the data were randomly allocated to training and 20% to network testing. Meanwhile, in the SFL algorithm, 75% of the data were used for training and 25% for testing. The coefficient of determination (R2) obtained between the observed and estimated values in this model for the thrust force and cutterhead torque was 0.19 and 0.52, respectively. The results showed that the SFL outperformed the other models in predicting the target parameters. In this method, the R2 obtained between observed and predicted values for thrust force and cutterhead torque is 0.73 and 0.63, respectively. The sensitivity analysis results show that the internal friction angle (φ) and standard penetration number (SPT) have the greatest impact on thrust force. Also, earth pressure and overburden thickness have the highest effect on cutterhead torque.

키워드

과제정보

The authors are grateful to the site engineers for their kind cooperation and for providing field data.

참고문헌

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