Predicting tunnel boring machine performance through a new model based on the group method of data handling

Ardalan H, Eslami A, Nariman-Zadeh N (2009) Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech 36:616–625

Bamford WE (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Fifth Australian Tunnelling Conference: State of the Art in Underground Development and Construction; Preprints of Papers. Institution of Engineers, Australia, p 218

Bejarbaneh BY, Bejarbaneh EY, Amin MFM et al (2016) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-016-0983-2

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

Bruines P (1998) Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate. Mem Cent Eng Geol Netherlands, Delft 202

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

Farlow SJ (1984) Self-organizing methods in modeling: GMDH type algorithms. Dekker, New York

Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunnels 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 Tech 30:110–123

Fattahi H (2016) Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm, a technique for estimation of TBM penetration rate. Iran Univ Sci Technol 6:159–171

Ghaleini EN, Koopialipoor M, Momenzadeh M, Sarafraz ME, Mohamad ET, Gordan B (2018) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Engineering with Computers, 1–12

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

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

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

Gordan B, Jahed Armaghani D, Adnan AB, Rashid ASA (2016) A new model for determining slope stability based on seismic motion performance. Soil Mech Found Eng 53:344–351. https://doi.org/10.1007/s11204-016-9409-1

Graham PC (1976) Rock exploration for machine manufacturers. Exploration for Rock Engineering 1:173–180

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

Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016a) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32. https://doi.org/10.1007/s00366-016-0447-0

Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016b) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75. https://doi.org/10.1007/s12665-016-5503-y

Hasanipanah M, Jahed Armaghani D, Amnieh HB, Koopialipoor M. Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng 1–14. https://doi.org/10.1007/s10706-018-0459-1

Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River

Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 1:364–378

Jahed Armaghani D, Hajihassani M, Bejarbaneh BY, Marto A, Mohamad ET (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498

Jahed Armaghani D, Mohamad ET, Momeni E, Narayanasamy MS (2015) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on main range granite. Bull Eng Geol Environ 74:1301–1319

Jahed Armaghani D, Mohamad ET, Hajihassani M, et al (2016a) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput 32. https://doi.org/10.1007/s00366-015-0410-5

Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016b) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186. https://doi.org/10.1016/j.ijrmms.2016.03.018

Jahed Armaghani D, Mohamad ET, Narayanasamy MS et al (2017a) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Tech 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009

Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017b) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28. https://doi.org/10.1007/s00521-015-2072-z

Jamali A, Nariman-Zadeh N, Darvizeh A et al (2009) Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng Appl Artif Intell 22:676–687

JAVAD G, NARGES T (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Technol 20:727–733

Jekabsons G, Lavendels J (2008) A comparison of heuristic methods for polynomial regression model induction. Math Model Anal 13:17–27

Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the S u–N SPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104:144–155

Khandelwal M, Jahed Armaghani D (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. https://doi.org/10.1007/s10706-015-9970-9

Koopialipoor M, Jahed Armaghani D, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ 1–10. https://doi.org/10.1007/s10064-017-1116-2

Koopialipoor M, Fallah A, Jahed Armaghani D, Azizi A, Mohamad ET (2018a) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 1–14. https://doi.org/10.1007/s00366-018-0596-4

Koopialipoor M, Jahed Armaghani D, Hedayat A, Marto A, Gordan B (2018b) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, 1–17

Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H (2015) Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found 55:1335–1345

Madala HR, Ivakhnenko AG (1994) Inductive learning algorithms for complex systems modeling. Crc press, Boca Raton

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

Mahdiyar A, Hasanipanah M, Jahed Armaghani D, et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput:1–11. https://doi.org/10.1007/s00366-016-0499-1

Mehrara M, Moeini A, Ahrari M, Erfanifard A (2009) RETRACTED: investigating the efficiency in oil futures market based on GMDH approach. Expert Syst Appl 36:7479–7483

Minh VT, Katushin D, Antonov M, Veinthal R (2017) Regression models and fuzzy logic prediction of TBM penetration rate. Open Eng 7:60–68

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 (pp. 442–447)

Mohamad ET, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Modell Simulations 5:2501–2506

Mohamad ET, Jahed Armaghani D, Ghoroqi M, et al (2017) Ripping production prediction in different weathering zones according to field data. Geotech Geol Eng 1–19. https://doi.org/10.1007/s10706-017-0254-4

Najafzadeh M, Barani G-A, Kermani MRH (2013) GMDH based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106

Nariman-Zadeh N, Darvizeh A, Darvizeh M, Gharababaei H (2002) Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition. J Mater Process Technol 128:80–87

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 & Exhibit, From the Mine to the Market, Now It’s Global, Seattle, WA, USA. p 297–302

Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBMs. In: Proceedings of the rapid excavation and tunneling conference. Society for Mining, Metallogy & Exploration, Inc, p 793

Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. In: International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. Elsevier, p 361–366

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

Salimi A, Rostami J, Moormann C, Delisio A (2016) Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunn Undergr Sp Tech 58:236–246

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

Sato K, Gong F, Itakura K (1991) Prediction of disc cutter performance using a circular rock cutting ring. In: Proceedings 1st international mine mechanization and automation symposium

Shams S, Monjezi M, Majd VJ, Jahed Armaghani D (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832

Shao C, Li X, Su H (2013) Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine. In: International Conference on Intelligent Robotics and Applications. Springer, p 409–416

Shijing W, Bo Q, Zhibo G (2006) The time and cost prediction of tunnel boring machine in tunnelling. Wuhan Univ J Nat Sci 11:385–388

Shooshpasha I, MolaAbasi H (2012) Prediction of Liquefaction Induced Lateral Displacements Using Plynomial Neural Networks and Genetic Algorithms. In: 15th World Conference on Earthquake Engineering, Lisbon, Portugal

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, p 947–954

Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28:1–12

Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38(2):269–284

Snowdon RA, Ryley MD, Temporal J (1982) A study of disc cutting in selected British rocks. In: International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. Elsevier, p 107–121

Sohani A, Sayyaadi H, Hoseinpoori S (2016) Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network. Int J Refrig 69:186–204

Srinivasan D (2008) Energy demand prediction using GMDH networks. Neurocomputing 72:625–629

Sundaram M (2007) The effects of ground conditions on TBM performance in tunnel excavation–A case history

Sundaram NM, Rafek AG, Komoo I (1998) The influence of rock mass properties in the assessment of TBM performance. In: Proceedings of the 8th IAEG Congress, Vancouver, British Columbia, Canada. p 3553–3559

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

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 6:1215–1227

Ulusay R, Hudson JA ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628

Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Applic 22:1685–1693

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. Doctoral dissertation, Colorado School of Mines

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

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, 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

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

Yagiz S, Sezer E, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods Geomech 36(14):1636–1650

Yılmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng

Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S(2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3–4):141–158