Performance prediction of tunnel boring machine through developing a gene expression programming equation

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Thesis, Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA, p 289 (unpublished)

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(4):808–814

Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. Int J Rock Mech Mining Sci Geomech Abstr 12:361–366

Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunn Tunn Int 12(6):22–25

Snowdon RA, Ryley MD, Temporal J (1982) A study of disc cutting in selected British rocks. Int J Rock Mech Min Sci 19:107–121

Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBM. In: Bowerman LD et al. (eds) Proceedings of RETC, Boston, MA, pp 793–809

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

Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock conditions. Tunn Undergr Space Technol 23(3):326–339

Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Mining Sci 46(1):8–18

Bruines P (1998) Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate. Memoirs of the Centre of Engineering Geology in The Netherlands, Delft, no. 173, p 202. ISSN: 1386-5072

Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23(3):249–262

Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233

Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci. doi: 10.1007/s12517-015-1952-y

Faradonbeh RS, Armaghani DJ, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75(3):993–1006

Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45

Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119

Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res 63(1):32–38

Singh TN, Verma AK, Singh V, Sahu A (2005) Slake durability study of shaly rock and its predictions. Environ Geol 47(2):246–253

Armaghani DJ, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8(11):9647–9665

Armaghani DJ, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32(1):109–121

Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2016) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput. doi: 10.1007/s00366-016-0486-6

Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169

Sharma LK, Vishal V, Singh TN (2017) Predicting CO2 permeability of bituminous coal using statistical and adaptive neurofuzzy analysis. J Nat Gas Sci Eng 42:216–225

Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Space Technol 15(3):259–269

Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19(6):597–605

Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48(3):427–433

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

Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Env 73(1):23–35

Yagiz S, Karahan H (2015) Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. Int J Rock Mech Min Sci 80:308–315

Gholamnejad J, Tayarani N (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Technol (China). 20(5):727–733

Simoes MG, Kim T (2006) Fuzzy modeling approaches for the prediction of machine utilization in hard rock tunnel boring machines. In: Industry applications conference, 2006. 41st IAS Annual Meeting. Conference record of the 2006 IEEE. IEEE, pp 947–954

Mikaeil R, Naghadehi MZ, Sereshki F (2009) Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions. Tunn Undergr Space Technol 24(5):500–505

Eftekhari M, Baghbanan A, Bayati M (2010) Predicting penetration rate of a tunnel boring machine using artificial neural network. In: ISRM international symposium-6th asian rock mechanics symposium. International Society for Rock Mechanics, pp 1–7

Gholami M, Shahriar K, Sharifzadeh M, Hamidi JK (2012) A comparison of artificial neural network and multiple regression analysis in TBM performance prediction. In: ISRM regional symposium-7th Asian rock mechanics symposium. International Society for Rock Mechanics

Salimi A, Esmaeili M (2013) Utilizing of linear and non-linear prediction tools for evaluation of penetration rate of tunnel boring machine in hard rock condition. Int J Min Min Eng 4(3):249–264

Torabi SR, 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(4):1215–1227

Shao C, Li X, Su H (2013) Performance prediction of hard rock TBM based on extreme learning machine. Intelligent robotics and applications. Springer, Berlin, pp 409–416

Yavari M, Mahdavi S (2005) Prediction of penetration rate of TBM using ANN. In: National mining conference, 1–3 Feb 2005, Iran, pp 1–10

Oraee K, Khorami MT, Hosseini N (2012) Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (ANFIS). In: Proceeding of SME annual meeting and exhibit, from the mine to the market, now it’s global, Seattle, WA, USA, pp 297–302

Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N, Yagiz S (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43

Shirani Faradonbeh R, Jahed Armaghani D, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Env. doi: 10.1007/s10064-016-0872-8

Baykasoglu A, Gullu H, Canakci H, Ozbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

Çanakcı H, Baykasoğlu A, Güllü H (2009) Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput Appl 18(8):1031–1041

Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mech Geotech Eng 5(4):325–329

Dindarloo SR, Siami-Irdemoosa E (2015) Estimating the unconfined compressive strength of carbonate rocks using gene expression programming. Eur J Sci Res 135(3):309–316

Ahangari K, Moeinossadat SR, Behnia D (2015) Estimation of tunnelling-induced settlement by modern intelligent methods. Soils Found 55(4):737–748

Khandelwal M, Armaghani DJ, Faradonbeh RS, Ranjith PG, Ghoraba S (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci 75(9):1–13

Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J South Afr Inst Min Metall 115(7):637–643

Shirani Faradonbeh R, Jahed Armaghani D, Monjezi M, Mohamad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264

Ferreira C (2001) Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 13:87–129

Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087

Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141:92–113

Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput 32(4):717–728

Shirani Faradonbeh R, Armaghani DJ, Amnieh HB, Mohamad ET (2016) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl. doi: 10.1007/s00521-016-2537-8

Steeb W-HH (2000) The nonlinear workbook: chaos, fractals, cellular automata. Neural networks, genetic algorithms, fuzzy logic with C++ and reduce programs. World Scientific Press, Singapore

Jahed Armaghani D, Faradonbeh RS, Rezaei H, Rashid ASA, Amnieh HB (2016) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl. doi: 10.1007/s00521-016-2618-8

Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning ‘addison-wesley, 1989, Reading

Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, New York

Khandelwal M, Faradonbeh RS, Monjezi M, Armaghani DJ, Majid MZBA, Yagiz S (2016) Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Eng Comput. doi: 10.1007/s00366-016-0452-3

Mogana SN (2007) The effects of ground conditions on TBM performance in tunnel excavation—a case history. In: Proceedings of the 10th Australia New Zealand conference on Geomechanics 2007, pp 442–447

Mogana SN, Rafek AG, Komoo I (1998) The influence of rock mass properties in the assessment of TBM performance. In: Moore D, Hungr O (eds) 8th IAEG Congr. Vancouver, Balkema, Rotterdam, pp 3553–3559

Wu S, Qian B, Gong Z (2006) The time and cost prediction of tunnel boring machine in tunneling. Wuhan Univ J Nat Sci 11(2):385–398

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

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

ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods, International Society for Rock Mechanics. ISRM Turkish National Group, Ankara

Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Env. doi: 10.1007/s10064-014-0687-4

Swingler K (1996) Applying neural networks: a practical guide. Academic, New York

Tonnizam Mohamad E, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl. doi: 10.1007/s00521-016-2359-8

Inc SPSS (2007) SPSS for windows (version 160). SPSS Inc, Chicago