Geomechanics and Engineering

  • 제22권1호
  • /
  • Pages.81-95
  • /
  • 2020
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  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of unconfined compressive strength ahead of tunnel face using measurement-while-drilling data based on hybrid genetic algorithm

  • Liu, Jiankang (Graduate School of Engineering, Nagasaki University) ;
  • Luan, Hengjie (College of Energy and Mining Engineering, Shandong University of Science and Technology) ;
  • Zhang, Yuanchao (Graduate School of Engineering, Nagasaki University) ;
  • Sakaguchi, Osamu (Department of Civil Engineering, Konoike Construction Co., Ltd.) ;
  • Jiang, Yujing (Graduate School of Engineering, Nagasaki University)
  • 투고 : 2020.03.19
  • 심사 : 2020.06.01
  • 발행 : 2020.07.10
⟨ 이전 논문 다음 논문 ⟩

초록

Measurement of the unconfined compressive strength (UCS) of the rock is critical to assess the quality of the rock mass ahead of a tunnel face. In this study, extensive field studies have been conducted along 3,885 m of the new Nagasaki tunnel in Japan. To predict UCS, a hybrid model of artificial neural network (ANN) based on genetic algorithm (GA) optimization was developed. A total of 1350 datasets, including six parameters of the Measurement-While- Drilling data and the UCS were considered as input and output parameters respectively. The multiple linear regression (MLR) and the ANN were employed to develop contrast models. The results reveal that the developed GA-ANN hybrid model can predict UCS with higher performance than the ANN and MLR models. This study is of great significance for accurately and effectively evaluating the quality of rock masses in tunnel engineering.

키워드

과제정보

Thank the Konoike Construction for providing on-site investigation guidance and data analysis support. In addition, this work was supported by the China Scholarship Council (No. 201708370104).

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피인용 문헌

  1. Evaluation of geological conditions and clogging of tunneling using machine learning vol.25, pp.1, 2020, https://doi.org/10.12989/gae.2021.25.1.059