Rock and Soil Mechanics ›› 2021, Vol. 42 ›› Issue (2): 519-528.doi: 10.16285/j.rsm.2020.5164

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Predicting TBM penetration rate with the coupled model of partial least squares regression and deep neural network

YAN Chang-bin1, WANG He-jian1, YANG Ji-hua2, CHEN Kui3, ZHOU Jian-jun3, GUO Wei-xin2   

  1. 1. School of Civil Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China 2. Yellow River Engineering Consulting Co., Ltd., Zhengzhou, Henan 450003, China 3. State Key Laboratory of Shield Machine and Boring Technology, China Railway Tunnel Group Co., Ltd., Zhengzhou, Henan 450001, China
  • Online:2021-02-11 Published:2021-06-18
  • About author:YAN Chang-bin, male, born in 1979, PhD, Professor, research interests: geotechnical and underground engineering.
  • Supported by:
    the National Natural Science Foundation of China (41972270,U1504523), the Key Science and Technology Research Project of Henan (182102210014) and the Opening Foundation of State Key Laboratory of Shield Machine and Boring Technology (SKLST-2019-K06).

Abstract: The scientific prediction of the TBM penetration rate is of great significance to the selection of hydraulic tunnel construction methods, construction schedule and cost estimation. In view of the high nonlinearity, fuzziness and complexity of TBM excavation process, and in order to improve the prediction accuracy and computational efficiency, the partial least squares regression (PLSR) has been applied to extract the principal components of the influencing parameters. Then the deep neural network (DNN) is employed to train and forecast the TBM penetration rate. A prediction model of TBM penetration rate based on the coupled method of PLSR and DNN is proposed. Based on the measured data of the double-shield TBM construction of a water conveyance tunnel in the Lanzhou water source construction project, six impact parameters including the rock uniaxial compressive strength, rock uniaxial tensile strength, cutter head thrust, cutter head speed, rock mass integrity coefficient and rock Cerchar abrasiveness index are selected to verify the prediction reasonability of the model. The fitting and prediction accuracy of the different prediction methods are compared and analyzed. The research results show that the PLSR can effectively overcome the problem of multiple collinearity between the independent variables. The extracted principal components are trained as the input layer of the DNN, which simplifies the structure of the neural network. The PLSR-DNN coupled model effectively avoids the over-fitting and inadequate fitting problems. It has the characteristics of fast convergence, stable solution and high fitting accuracy. The average relative fitting error of the PLSR-DNN prediction model is 2.96%, and the average relative prediction error is 3.27%. The fitting accuracy and prediction accuracy of the PLSR-DNN prediction model is significantly higher than those of PLSR model alone, BP neural network model and SVR model, respectively.

Key words: tunnel boring machine, penetration rate, partial least squares regression, deep neural network, coupling prediction model