Geomechanics and Engineering

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Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns

  • Liu, Jiankang (College of Energy and Mining Engineering, Shandong University of Science and Technology) ;
  • Jiang, Yujing (Graduate School of Engineering, Nagasaki University) ;
  • Zhang, Yuanchao (Graduate School of Engineering, Nagasaki University) ;
  • Sakaguchi, Osamu (Department of Civil Engineering, Konoike Construction Co., Ltd.)
  • Received : 2020.02.01
  • Accepted : 2021.06.08
  • Published : 2021.06.25
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Abstract

In tunnel engineering, the selection of tunnel support patterns should be estimated accurately to ensure stability of the tunnel, which may be caused by unexpected hazardous zones ahead of tunnel face. This study presents a method to estimate the selection of support patterns using artificial neural network (ANN) based on 318, 649 Measurement While Drilling (MWD) data. Controlled trials are conducted considering different input layer sizes and hidden layer sizes to obtain the optimal ANN model. Combinations of 6 feature parameters including penetration rate (PR), hammer pressure (HP), rotation pressure (RP), feed pressure (FP), hammer frequency (HF) and specific energy (SE) correspond to the different input layer sizes of the ANN. Average accuracy (A), average computing-time (T), sensitivity and stability are adopted as the performance index. The results show that a strong correlation exists between MWD data and support patterns. The combination of 6 feature parameters outperforms the subset of the entire feature parameters in terms of A, sensitivity and stability. The ANN model with the combination of PR, HP, RP, FP, HF and SE as the input feature parameters has the highest estimation stability. The ANN model with 6 feature parameters and one hidden layer with 30 nodes is proposed as optimal model considering all indices. The results confirm that it is feasible to estimate support patterns ahead of tunnel face using ANN based on MWD data.

Keywords

Acknowledgement

The authors gratefully acknowledge support of Civil Engineering Department, Technical Division, Konoike Construction Japan for providing field data and sharing experience on tunnel construction.

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