Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (U...

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Bibliographic Details
Published in:Open Engineering (Warsaw) 2017-03, Vol.7 (1), p.60-68
Main Authors: Minh, Vu Trieu, Katushin, Dmitri, Antonov, Maksim, Veinthal, Renno
Format: Article
Language:English
Subjects:
Alpha
BTS
DPW
fuzzy logic model
regression models
rock engineering properties
ROP
TBM
UCS
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Summary:This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R ) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
ISSN:2391-5439
2391-5439
DOI:10.1515/eng-2017-0012