Potential efficacy and application of a new statistical meta based-model to predict TBM performance

ABSTRACT

This study constructs and verifies a new statistical meta based-model to predict tunnel-boring machine (TBM) performance, namely, polynomial chaos expansion (PCE). To test the validity of the proposed PCE, two well-known mathematical models, namely, response surface method (RSM) and multivariate adaptive regression spline (MARS) were developed. According to the results, it can be found that the PCE model, with a coefficient of determination (R²) of 0.843, was superior in comparison with the RSM and MARS models as well as those formerly presented in the literature for the same database and rock conditions.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Networks; AR: Advance Rate; BI: Rock Brittleness; BTS: Brazilian Tensile Strength; CP: Cutterhead Power; CT: Cutterhead Torque; d: Modified Agreement Index; DNN: Deep Neural Networks; DPW: Distance between Planes of Weakness; ICA: Imperialist Competitive Algorithm; MAE: Mean Absolute Error; MARS: Multivariate Adaptive Regression Spline; NSE: Modified Nash and Sutcliffe Efficiency; NTNU: Norwegian Institute of Technology; PCE: Polynomial Chaos Expansion; PR: Penetration Rate; PSI: Point Strength Index; PSO: Particle Swarm Optimisation; R2: Coefficient of Determination; RF: Random Forests; RMR: Rock Mass Rating; RMSE: Root Mean Square Error; RQD: Rock Quality Designation; RSM: Response Surface Method; RSR: Rock Structure Rating; SE: Specific Energy; SVR: Support Vector Regression; TBM: Tunnel-Boring Machine; TF: Thrust Force; UCS: Uniaxial Compressive Strength; WZ: Weathering Zone; α: Planes Of weakness.

KEYWORDS:

• TBM performance
• polynomial chaos expansion
• multivariate adaptive regression spline
• response surface method

Acknowledgments

The authors would like to thank dear Professor Saffet Yagiz for providing the information and facilities required for conducting this research. In addition, this work was supported by University of Zabol under Grant No. UOZ-GR-9618-1 and UOZ-GR-9719-1.

Disclosure statement

No potential conflict of interest was reported by the author(s).