Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Sp Technol 23:600–608

Aeberli U, Wanner H (1978) On the influence of geologic conditions at the application of tunnel boring machines. In: 3rd international conference international association of engineering geology, Madrid vol 2, pp 7–14

Bamford W (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Proceedings of the 5th Australian tunneling conference, Melbourne, Australian, pp 9–22

Barton NR (2000) TBM tunnelling in jointed and faulted rock. CRC Press, Balkema, Rotterdam

Benardos A, Kaliampakos D (2004a) A methodology for assessing geotechnical hazards for TBM tunnelling—illustrated by the Athens Metro, Greece. Int J Rock Mech Min Sci 41:987–999

Benardos A, Kaliampakos D (2004b) Modelling TBM performance with artificial neural networks. Tunn Undergr Sp Technol 19:597–605

Blindheim O (1979) Boreability predictions for tunneling. Ph.D. Thesis

Bruland A (1998) Hard rock tunnel boring, Ph.D. Thesis. Ph.D. Thesis, Norwegian University of Science and Technology

Cassinelli F, Cina S, Innaurato N, Mancini R, Sampaolo A (1982) Power consumption and metal wear in tunnel-boring machines: analysis of tunnel-boring operation in hard rock. Tunneling 82:73–81

Dollinger G, Handewith H, Breeds C (1998) Use of the punch test for estimating TBM performance. Tunn Undergr Sp Technol 13:403–408

Farmer I, Glossop N (1980) Mechanics of disc cutter penetration. Tunn Tunn 12:22–25

Farrokh E, Rostami J, Laughton C (2012) Study of various models for estimation of penetration rate of hard rock TBMs. Tunn Undergr Sp Technol 30:110–123

Gehring K (2009) The influence of TBM design and machine features on performance and tool wear in rock. Der Einfluss von TBM-Konstruktion und Maschineneigenschaften auf Leistung und Werkzeugverbrauch in Gestein Geomech Tunnel 2:140–155

Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Environ 73:23–35

Graham P (1976) Rock exploration for machine manufacturers. In: Bieniawski ZT (ed) Exploration for rock engineering. Balkema, Rotterdam, pp 173–180

Grima MA, Bruines P, Verhoef P (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259–269

Hamidi JK, Shahriar K, Rezai B, Rostami J (2010) Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunn Undergr Sp Technol 25:333–345

Hassanpour J, Rostami J, Khamehchiyan M, Bruland A (2009) Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel. GeoMech GeoEng: Int J 4:287–297

Hassanpour J, Rostami J, Zhao J (2011) A new hard rock TBM performance prediction model for project planning. Tunn Undergr Sp Technol 26:595–603

Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87:861–870

Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

Li C, Zhou J (2011) Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm. Energy Convers Manag 52:374–381

Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229

Moradi MR, Farsangi MAE (2014) Application of the risk matrix method for geotechnical risk analysis and prediction of the advance rate in rock TBM tunneling. Rock Mech Rock Eng 47:1951–1960

Rashedi E, Nezamabadi-Pour H, Saryazdi S, Farsangi MM (2007) Allocation of static var compensator using gravitational search algorithm. world 1:10

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24:117–122

Ribacchi R, Fazio AL (2005) Influence of rock mass parameters on the performance of a TBM in a gneissic formation (Varzo Tunnel). Rock Mech Rock Eng 38:105–127

Rostami J (1997) Development of a force estimation model for rock fragmentation with disc cutters through theoretical and physical measurement of crushed zone pressure. Ph.D Dissertation, Colorado School of Mines, Golden, Colorado

Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBMs. In: Proceedings of the rapid excavation and tunneling conference, Boston. Society for mining, metallogy and exploration, INC, p 793

Salimia A, Moormanna C, Singhb T, Jainc P (2015) TBM performance prediction in rock tunneling using various artificial intelligence algorithms. In: 11th Iranian and 2nd regional tunnelling conference Iran

Sanio H (1985) Prediction of the performance of disc cutters in anisotropic rock. Int J Rock Mech Min Sci 22:153–161

Sapigni M, Berti M, Bethaz E, Busillo A, Cardone G (2002) TBM performance estimation using rock mass classifications. Int J Rock Mech Min Sci 39:771–788

Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a GSA–SVM hybrid system. Math Comput Model 57:270–278

Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9:625–631

Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces vol 3. ICSI Berkeley

Tarkoy PJ (1973) Predicting tunnel boring machine (TBM) penetration rates and cutter costs in selected rock types. In: Ninth Canadian rock mechanics symposium, Montreal

Torabi S, Shirazi H, Hajali H, Monjezi M (2013) Study of the influence of geotechnical parameters on the TBM performance in Tehran-Shomal highway project using ANN and SPSS. Arab J Geosci 6:1215–1227

Vapnik V (1999) The nature of statistical learning theory. Springer, Berlin

Wang J, Li T, Ren R (2010) A real time idss based on artificial bee colony-support vector machine algorithm. In: Advanced computational intelligence (IWACI), 2010 third international workshop on, 2010. IEEE, pp 91–96

Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65–70

Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM Model basic penetration for hard rock tunneling machines. Ph.D. Thesis, Colorado School of Mines, Golden, Colo

Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Sp Technol 23:326–339

Yagiz S (2009) Assessment of brittleness using rock strength and density with punch penetration test. Tunn Undergr Sp Technol 24:66–74

Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427–433

Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808–814

Yin M, Hu Y, Yang F, Li X, Gu W (2011) A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering. Expert Syst Appl 38:9319–9324

Zhang W, Niu P, Li G, Li P (2013) Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowl Based Syst 39:34–44

Zhao Z, Gong Q, Zhang Y, Zhao J (2007) Prediction model of tunnel boring machine performance by ensemble neural networks. GeoMech GeoEng: Int J 2:123–128