PREDICTION OF TBM PENETRATION RATE USING SUPPORT VECTOR MACHINE
Abstract
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.
References
ADOKO, A. C.; GOKCEOGLU, C.; YAGIZ, S. Bayesian prediction of TBM penetration rate in rock mass. Engineering Geology, 226, 245–256, 2017, Available from: https://doi.org/10.1016/j.enggeo.2017.06.014
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: https://doi.org/10.3233/AJW190006
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: https://doi.org/10.1016/j.jrmge.2019.01.002
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: https://doi.org/10.1016/j.tust.2018.02.012
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: https://doi.org/10.1016/j.tust.2005.12.016
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: https://doi.org/10.1016/0148-9062(83)91823-5.
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: https://doi.org/10.3182/20130708-3-CN-2036.00105
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: https://doi.org/10.1080/17486020903174303
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: https://doi.org/10.1016/j.tust.2011.04.004
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: https://doi.org/10.1016/j.tust.2010.01.008
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: https://doi.org/10.1016/j.tust.2013.05.002
MATLAB and Statistics Toolbox Release. The MathWorks, Inc., Natick, Massachusetts, United States, 2018.
MICROSOFT CORPORATION. Microsoft Excel, Available at: https://office.microsoft.com/excel, 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: https://doi.org/10.1007/s00603-004-0032-5.
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: https://doi.org/10.1016/j.tust.2017.06.015
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: https://doi.org/10.1016/j.neunet.2019.05.023
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: https://doi.org/10.1016/j.oceaneng.2019.106488
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: https://doi.org/10.1016/j.tust.2007.04.011
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: https://doi.org/10.1016/j.engappai.2009.03.007
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: https://doi.org/10.1016/j.ijrmms.2011.02.013
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: https://doi.org/10.1016/j.ijrmms.2015.09.019
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: https://doi.org/10.1007/s10706-018-0570-3
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: https://doi.org/10.1016/j.measurement.2018.05.049
ZHANG, H.; GAO, M. The Application of Support Vector Machine (SVM) Regression Method in Tunnel Fires. Procedia Engineering, 211, 1004–1011, 2019, Available from: https://doi.org/10.1016/j.proeng.2017.12.103
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: https://doi.org/10.1016/j.swevo.2019.100560
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: https://doi.org/10.1007/s00603-018-1667-y
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: https://doi.org/10.3233/AJW190006
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: https://doi.org/10.1016/j.jrmge.2019.01.002
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: https://doi.org/10.1016/j.tust.2018.02.012
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: https://doi.org/10.1016/j.tust.2005.12.016
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: https://doi.org/10.1016/0148-9062(83)91823-5.
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: https://doi.org/10.3182/20130708-3-CN-2036.00105
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: https://doi.org/10.1080/17486020903174303
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: https://doi.org/10.1016/j.tust.2011.04.004
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: https://doi.org/10.1016/j.tust.2010.01.008
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: https://doi.org/10.1016/j.tust.2013.05.002
MATLAB and Statistics Toolbox Release. The MathWorks, Inc., Natick, Massachusetts, United States, 2018.
MICROSOFT CORPORATION. Microsoft Excel, Available at: https://office.microsoft.com/excel, 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: https://doi.org/10.1007/s00603-004-0032-5.
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: https://doi.org/10.1016/j.tust.2017.06.015
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: https://doi.org/10.1016/j.neunet.2019.05.023
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: https://doi.org/10.1016/j.oceaneng.2019.106488
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: https://doi.org/10.1016/j.tust.2007.04.011
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: https://doi.org/10.1016/j.engappai.2009.03.007
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: https://doi.org/10.1016/j.ijrmms.2011.02.013
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: https://doi.org/10.1016/j.ijrmms.2015.09.019
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: https://doi.org/10.1007/s10706-018-0570-3
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: https://doi.org/10.1016/j.measurement.2018.05.049
ZHANG, H.; GAO, M. The Application of Support Vector Machine (SVM) Regression Method in Tunnel Fires. Procedia Engineering, 211, 1004–1011, 2019, Available from: https://doi.org/10.1016/j.proeng.2017.12.103
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: https://doi.org/10.1016/j.swevo.2019.100560
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: https://doi.org/10.1007/s00603-018-1667-y
Published
05/07/2020
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: <http://geosaberes.ufc.br/geosaberes/article/view/1048>. Date accessed: 22 nov. 2024.
doi: https://doi.org/10.26895/geosaberes.v11i0.1048.
Section
ARTICLES
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy Proposal for Free Access Journals
Authors who publish in this journal agree to the following terms:
a. Authors retain the copyright and grant the journal the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution License which allows the sharing of the work with acknowledgment of the authorship of the work and initial publication in this journal.
b. Authors are authorized to take additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (eg publish in institutional repository or as a book chapter), with acknowledgment of authorship and initial publication in this journal.
c. Authors are allowed and encouraged to publish and distribute their work online (eg in institutional repositories or on their personal page) at any point before or during the editorial process, as this can generate productive changes, as well as increase the impact and The citation of published work (See The Effect of Free Access).
