One of the most important issues in mechanized excavating is to predict the TBM penetration rate. Understanding the factors influencing the rate of penetration is important, which allows for a more accurate estimation of the stopping and excavating times and operating costs. In this study, Input and output parameters including Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Peak Slope Index (PSI), Distance between Planes of Weakness (DPW), Alpha angle and Rate of Penetration (ROP) (m/hr) in the Queens Water Tunnel using support vector machine .Results showed that prediction of penetration rate for Support Vector Machine (SVM) method is R2 = 0.9678 and RMSE = 0.064778, According to the results, Support Vector Machine (SVM) is effective and has high accuracy.

Author Biographies

Alireza Afradi, Islamic Azad University, Iran

PhD Candidate, Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.

Arash Ebrahimabadi, Islamic Azad University, Iran

Associate Professor, Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.

Tahereh Hallajian, Islamic Azad University, Iran

Assistant Professor, Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.


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How to Cite
AFRADI, Alireza; EBRAHIMABADI, Arash; HALLAJIAN, Tahereh. PREDICTION OF TBM PENETRATION RATE USING SUPPORT VECTOR MACHINE. Geosaberes, Fortaleza, v. 11, p. 467 - 479, july 2020. ISSN 2178-0463. Available at: <>. Date accessed: 19 june 2024. doi: