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A mechanical method for predicting TBM penetration rates

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Abstract

In the tunnel boring machine (TBM) excavation process, the accurate prediction of the TBM performance, especially the penetration rate, is of great significance to the in time planning, cost control, and safety judgment. In this paper, we propose a prediction method and its corresponding model of penetration rate based on a mechanical analysis. The variables of the method included the design parameters (tunnel radius and cutter arrangement), the operating parameters (the thrust, torque and revolution per minute), and the tensile strength. The rock breakage depth of a single cutter was calculated using the force balance. Then, the rock breakage volume per revolution was obtained by summing up the rock breakage of each cutter based on the cutter arrangement. Finally, based on the premise of knowing the rock breakage volume per revolution, the numerical solution of the penetration rate was acquired. On the basis of the field data from the 4th Section of the Water Supply Project from Songhua River, the accuracy of the model is verified by 59 samples, with a correlation coefficient of 0.64 between prediction and actual results and a mean absolute percentage error of 14.6%. Furthermore, a basic form of the PR prediction equation suitable for different projects was proposed using the single variable control method. For CREC188 TBM in particular, the undetermined coefficient values of the equation were determined using 7-fold cross-validation and the calculation equation was verified with another 50 samples with a correlation coefficient of 0.78 and a mean absolute percentage error of 10.9%.

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Abbreviations

α:

Strike and dip angle of the fractures and the TBM-driven direction

ANN:

Artificial neural network

BI:

Brittleness index

BTS:

Brazil tensile strength

CART:

Classification and regression trees

CHAID:

Chi-squared automatic interaction detection

CSM:

Colorado School of Mines

DPW:

Distance between plane of weakness

Fn :

The normal force

Fr :

The rolling force (Fr)

FL:

Fuzzy logic

GEP:

Gene expression programming

GMDH:

Group method of data handling

KNN:

K-nearest neighbor

MAPE:

Mean absolute percentage error

NN:

Neural network

NTNU:

Norwegian University of Science and Technology

PR:

Penetration rate

PSO:

Particle swarm optimization

QTBM :

Modified Q system

R:

Radius of the tunnel face

RIAT:

Rolling Indentation Abrasion Test

RME:

Rock mass classification

RMR:

Rock mass rating

RPM:

Revolutions per minute

RQD:

Rock quality designation

SVM:

Support vector machine

SVR:

Support vector regression

Th:

Thrust of the TBM cutterhead

Tor:

Torque of the TBM cutterhead

WZ:

Weathering zone

{di}:

Cutter arrangement

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Acknowledgments

The authors wish to thank the China Railway Tunnel Stock Company Limited for sharing their experiences of data gathering efforts in the field.

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (NSFC) (No. 51739007), The National Key Research and Development Program of China (No. 2016YFC0401805), the National Natural Science Foundation of China (NSFC) (No. U1806226), the Key Research and Development Program of Shandong Province (No Z135050009107), and the Interdisciplinary Development Program of Shandong University (No. 2017JC002).

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Authors and Affiliations

  1. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China

    Ruirui Wang

  2. Department of Mathematics, The Chinese University of Hong Kong, Central Ave, Hong Kong

    Xu Guo

  3. China Railway Engineering Equipment Group, Zhengzhou, Henan, China

    Jianbin Li & Liujie Jing

  4. Geotechnical and Structural Engineering Research Center, Shandong University, 17923 Jingshi Road, Jinan, China

    Jian Wang, Zhengyu Liu & Xinji Xu

Authors
  1. Ruirui Wang
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  2. Xu Guo
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  3. Jianbin Li
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  4. Jian Wang
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  5. Liujie Jing
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  6. Zhengyu Liu
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  7. Xinji Xu
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Correspondence to Xinji Xu.

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Responsible Editor: Atsushi Sainoki

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Wang, R., Guo, X., Li, J. et al. A mechanical method for predicting TBM penetration rates. Arab J Geosci 13, 335 (2020). https://doi.org/10.1007/s12517-020-05305-x

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