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

Keshtegar B. Hasanipanah M. Nguyen-Thoi T. Yagiz S. Bakhshandeh Amnieh H.
2021 Taylor and Francis Ltd.

International Journal of Mining, Reclamation and Environment
2021 #35 Issue 7 471 - 487 pp.

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.

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

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School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
School of Mining and Geosciences, Nazarbayev University, Nur-Sultan City, Kazakhstan
School of Mining, College of Engineering, University of Tehran, Tehran, Iran

School of Mechanical and Electrical Engineering
Institute of Research and Development
Division of Computational Mathematics and Engineering
Faculty of Civil Engineering
School of Mining and Geosciences
School of Mining

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