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.


ADOKO, A. C.; GOKCEOGLU, C.; YAGIZ, S. Bayesian prediction of TBM penetration rate in rock mass. Engineering Geology, 226, 245–256, 2017, Available from:

AFRADI A.; EBRAHIMABADI, A.; HALLAJIAN T. Prediction of the Penetration Rate and Number of Consumed Disc Cutters of Tunnel Boring Machines (TBMs) Using Artificial Neural Network (ANN) and Support Vector Machine (SVM)-Case Study: Beheshtabad Water Conveyance Tunnel in Iran. Asian Journal of Water, Environment and Pollution 16, 1, 49-57, 2019, Available from:

ARMAGHANI, D. J.; KOOPIALIPOOR, M.; MARTO, A.; YAGIZ, S. Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11(4), 779–789, 2019, Available from:

ARMETTI, G.; MIGLIAZZA, M. R.; FERRARI, F.; BERTI, A.; PADOVESE, P. Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy). Tunnelling and Underground Space Technology, 77, 115–126, 2018, Available from:

BARTON, N. TBM Tunneling in Jointed and Fault Rock. Rotterdam: Balkema, 2000.

BAMFORD, W. E. Rock test indices are being successfully correlated with tunnel boring machine performance. Proceedings, Fifth Australian Tunneling Conference, Sydney, 218-221, 1984.

BIENIAWSKI VON PREINL, Z. T.; CELADA TAMAMES, B.; GALERA FERNÁNDEZ, J. M.; ÁLVAREZ HERNÁNDEZ, M. Rock mass excavability indicator: New way to selecting the optimum tunnel construction method. Tunnelling and Underground Space Technology, 21(3–4), 237, 2006, Available from:

CASSINELLI, F.; CINA, S.; INNAURATO, N. Power consumption and metal wear in tunnel-boring machines: analysis of tunnel-boring operation in hard rock. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 20(1), A25, 1983, Available from:

FARMER, I. W.; GLOSSOP, N.H. Mechanics of disc cutter penetration. Tunnels and Tunnelling International, 12(6), 22-25, 1980.

GE, Y.; WANG, J.; LI, K. Prediction of hard rock TBM penetration rate using least square support vector machine. IFAC Proceedings Volumes, 46(13), 347–352, 2013, Available from:

HASSANPOUR, J.; ROSTAMI, J.; KHAMEHCHIYAN, M.; BRULAND, A. Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel. Geomechanics and Geoengineering, 4(4), 287–297, 2009, Available from:

HASSANPOUR, J., ROSTAMI, J., & ZHAO, J. A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, 26(5), 595–603, 2011, Available from:

IBM Corp. Released. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp., 2017.

INNAURATO N.; MANCINI R.; RONDENA E.; ZANINETTI A. Forecasting andeffective TBM performances in a rapid excavation of a tunnel in Italy, Seventh International Congress ISRM, Aachen, 1009-1014, 1991.

KHADEMI HAMIDI, J.; SHAHRIAR, K.; REZAI, B.; ROSTAMI, J. Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunnelling and Underground Space Technology, 25(4), 333–345, 2010, Available from:

LISLERUD, A. et al. Hard rock tunnel boring. Project Rep 1-83, Univ. Trondheim, Norwegian Institute of Technology, Division Construction Engineering, 159, 1983.

MAHDEVARI, S.; SHIRZAD HAGHIGHAT, H.; TORABI, S. R. A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunnelling and Underground Space Technology, 38, 59–68, 2013, Available from:

MATLAB and Statistics Toolbox Release. The MathWorks, Inc., Natick, Massachusetts, United States, 2018.

MICROSOFT CORPORATION. Microsoft Excel, Available at:, 2019.

RIBACCHI, R.; LEMBO-FAZIO, A. Influence of rock mass parameters on the performance of a TBM in a gneissic formation (Varzo Tunnel), Rock Mechanics and Rock Engineering, 38 (2), 105-127, 2005, Available from:

TARKOY, P. J. Prediction TBM penetration rates in selected rock types. Proceedings, Ninth Canadian Rock Mechanics Symposium, Montreal, 1973.

VAPNIK, V. The nature of statistical learning theory. New York: Springer: 1995.

VAPNIK, V. Statistical learning theory. New York: Wiley, 1998.

VERGARA, I. M.; SAROGLOU, C. Prediction of TBM performance in mixed-face ground conditions. Tunnelling and Underground Space Technology, 69, 116-124, 2017, Available from:

XU, B.; SHEN, S.; SHEN, F.; ZHAO, J. Locally linear SVMs based on boundary anchor points encoding. Neural Networks, 117, 274–284, 2019, Available from:

XU, H.; SOARES, C. G. Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated LS-SVM. Ocean Engineering, 191, 106488, 2019, Available from:

YAGIZ, S. Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnelling and Underground Space Technology, 23(3), 326-339, 2008, Available from:

YAGIZ, S.; GOKCEOGLU, C.; SEZER, E.; IPLIKCI, S. Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence, 22(4–5), 808–814, 2009, Available from:

YAGIZ, S.; KARAHAN, H. Prediction of hard rock TBM penetration rate using particle swarm optimization. International Journal of Rock Mechanics and Mining Sciences, 48(3), 427–433, 2011, Available from:

YAGIZ, S., & KARAHAN, H. Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. International Journal of Rock Mechanics and Mining Sciences, 80, 308–315, 2015, Available from:

YAGIZ, S.; GHASEMI, E.; ADOKO, A. C. Prediction of Rock Brittleness Using Genetic Algorithm and Particle Swarm Optimization Techniques. Geotechnical and Geological Engineering, 36(6), 3767–3777, 2018, Available from:

ZARE NAGHADEHI, M.; SAMAEI, M.; RANJBARNIA, M.; NOURANI, V. State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming. Measurement, 126, 46–57, 2018, Available from:

ZHANG, H.; GAO, M. The Application of Support Vector Machine (SVM) Regression Method in Tunnel Fires. Procedia Engineering, 211, 1004–1011, 2019, Available from:

ZHANG, J.; XING, L.; PENG, G.; YAO, F.; CHEN, C. A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-II. Swarm and Evolutionary Computation, 50, 100560, 2019, Available from:

ZHAO, Y.; YANG, H.; CHEN, Z.; CHEN, X.; HUANG, L.; LIU, S. Effects of Jointed Rock Mass and Mixed Ground Conditions on the Cutting Efficiency and Cutter Wear of Tunnel Boring Machine. Rock Mechanics and Rock Engineering, 52(5), 1303–1313, 2018, Available from:
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: 06 aug. 2020. doi: