Skip to main content
SpringerLink
Log in

Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification

  • Original Paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

Using the tunnel boring machine (TBM) in tunneling projects contributes significantly to increased efficiency and reducing the time of project implementation in comparison with the classical methods. Since the scheduled deadline is a major issue in the mechanized tunneling project, factors that affect the performance of TBM must be deeply considered in the assessment of tunneling operations. In the implementation of the mechanized tunneling project, a key variable is to predict the penetration rate of TBM. The main aim of this study is to predict the penetration rate of TBM in a novelty framework based on binary classification. For this purpose, the two most effective artificial intelligence (AI) techniques, namely a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) and also the group method of data handling (GMDH) were applied, and a valuable database composed of 2838 was collected from the Kerman water conveyance tunnel project. The values of three parameters including the rotation speed, torque, and thrust force were measured that were considered as input data, and the values of penetration rate were measured as output data. Finally, the best-developed models were able to predict the binary classification of the TBM penetration rate with a testing accuracy of 92% and 91.6% for GMDH and GOA-SVM, respectively. In addition, the results obtained from the sensitivity analysis indicated that the rotation speed had the highest impact on the predicted penetration rate and torque and thrust force had the subsequent maximum impact in descending order, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Afradi A, Ebrahimabadi A, Hallajian T (2019) 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 J Water Environ Pollut 16(1):49–57

    Article  Google Scholar 

  • Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2021). Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm. Geotechnical and Geological Engineering, 1–24.

  • 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

    Article  Google Scholar 

  • Armaghani DJ, Faradonbeh RS, Momeni E, Fahimifar A, Tahir MM (2018) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Engineering with Computers 34(1):129–141

    Article  Google Scholar 

  • Armaghani DJ, Hasanipanah M, Amnieh HB, Bui DT, Mehrabi P, Khorami M (2020) Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Engineering with Computers 36:1379–1391

    Article  Google Scholar 

  • Aryafar A, Mikaeil R, Haghshenas SS, Haghshenas SS (2018a) Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement 124:20–31

    Article  Google Scholar 

  • Aryafar A, Mikaeil R, Shafiee Haghshenas S, Shafiei Haghshenas S (2018b) Utilization of soft computing for evaluating the performance of stone sawing machines, Iranian Quarries. Int J Mining and Geo-Eng 52(1):31–36

    Google Scholar 

  • Aryafar A, Mikaeil R, Doulati Ardejani F, Shaffiee Haghshenas S, Jafarpour A (2019) Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. J Mining Environ 10(2):327–337

    Google Scholar 

  • Behnood A, Golafshani EM (2020) Machine learning study of the mechanical properties of concretes containing waste foundry sand. Constr Build Mater 243:118152

    Article  Google Scholar 

  • Bruland, A. (1998). Hard rock tunnel boring: vol 1–10 (Doctoral dissertation, Ph. D. thesis, Norwegian university of science and technology (NTNU), Trondheim, Norway).

  • Cassinelli, F. (1982). Power consumption and metal wear in tunnel-boring machines: analysis of tunnel-boring operation in hard rock.

  • Chen WH, Hsu SH, Shen HP (2005) Application of SVM and ANN for intrusion detection. Comput Oper Res 32(10):2617–2634

  • Cortes C, Vapnik V (1995) Support-Vector Networks Machine Learning 20(3):273–297

    Google Scholar 

  • Dag O, Karabulut E, Alpar R (2019) GMDH2: binary classification via GMDH-type neural network algorithms—R package and web-based tool. Int J Comput Intell Syst 12:649–660

    Article  Google Scholar 

  • Dormishi A, Ataei M, Mikaeil R, Khalokakaei R, Haghshenas SS (2019) Evaluation of gang saws’ performance in the carbonate rock cutting process using feasibility of intelligent approaches. Eng Sci Technol Int J 22(3):990–1000

    Google Scholar 

  • 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 and Rock Engineering.

  • Elbaz K, Shen SL, Zhou A, Yin ZY, Lyu HM (2021) Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering 7(2):238–251

    Article  Google Scholar 

  • Faradonbeh RS, Haghshenas SS, Taheri A, Mikaeil R (2020) Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Comput Appl 32(12):8545–8559

    Article  Google Scholar 

  • Farmer, I. W., & Glossop, N. H. (1980). Mechanics of disc cutter penetration. Tunnels Tunnelling;(United Kingdom), 12(6).

  • 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

    Article  Google Scholar 

  • Fiorini Morosini A, Shaffiee Haghshenas S, Shaffiee Haghshenas S, Geem ZW (2020) Development of a binary model for evaluating water distribution systems by a pressure driven analysis (PDA) approach. Appl Sci 10(9):3029

    Article  Google Scholar 

  • Fiorini Morosini A, Shaffiee Haghshenas S, Shaffiee Haghshenas S, Choi DY, Geem ZW (2021) Sensitivity analysis for performance evaluation of a real water distribution system by a pressure driven analysis approach and artificial intelligence method. Water 13(8):1116

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Gholami, M., Shahriar, K., Sharifzadeh, M., & Hamidi, J. K. (2012, January). 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 and Rock Engineering.

  • Gong QM, Jiao YY, Zhao J (2006) Numerical modelling of the effects of joint spacing on rock fragmentation by TBM cutters. Tunn Undergr Space Technol 21(1):46–55

    Article  Google Scholar 

  • Gong QM, Zhao J, Jiang YS (2007) In situ TBM penetration tests and rock mass boreability analysis in hard rock tunnels. Tunn Undergr Space Technol 22(3):303–316

    Article  Google Scholar 

  • Goodarzizad P, Mohammadi Golafshani E, Arashpour M (2021) Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm. Int J Construc Manag. https://doi.org/10.1080/15623599.2021.1927363

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Astarita V, Haghshenas AS (2020a) Feasibility of stochastic models for evaluation of potential factors for safety: a case study in southern Italy. Sustainability 12(18):7541

    Article  Google Scholar 

  • Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Gallelli V, Astarita V (2020b) Development of a binary classification model to assess safety in transportation systems using GMDH-type neural network algorithm. Sustainability 12(17):6735

    Article  Google Scholar 

  • Haghshenas SS, Haghshenas SS, Mikaeil R, Sirati Moghadam P, Haghshenas AS (2017a) A new model for evaluating the geological risk based on geomechanical properties-case study: the second part of emamzade hashem tunnel. Electron J Geotech Eng 22(01):309–320

    Google Scholar 

  • Haghshenas SS, Faradonbeh RS, Mikaeil R, Haghshenas SS, Taheri A, Saghatforoush A, Dormishi A (2019) A new conventional criterion for the performance evaluation of gang saw machines. Measurement 146:159–170

    Article  Google Scholar 

  • Haghshenas SS, Haghshenas SS, Geem ZW, Kim TH, Mikaeil R, Pugliese L, Troncone A (2021) Application of harmony search algorithm to slope stability analysis. Land 10(11):1250

    Article  Google Scholar 

  • Haghshenas, S.S.; Ozcelik, Y.; Haghshenas, S.S.; Mikaeil, R.; Moghadam (2017b) PS Ranking and assessment of tunneling projects risks using fuzzy MCDM (Case study: Toyserkan doolayi tunnel). In Proceedings of the 25th International Mining Congress and Exhibition of Turkey, Antalya, Turkey, 289–297.

  • Hamidi JK, Shahriar K, Rezai B, Rostami J (2010) Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunn Undergr Space Technol 25(4):333–345

    Article  Google Scholar 

  • Harandizadeh H, Armaghani DJ, Mohamad ET (2020) Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets. Neural Comput Appl 32(17):14047–14067

    Article  Google Scholar 

  • 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 4(4):287–297

    Article  Google Scholar 

  • Hassanpour J, Rostami J, Khamehchiyan M, Bruland A, Tavakoli HR (2010) TBM performance analysis in pyroclastic rocks: a case history of Karaj water conveyance tunnel. Rock Mech Rock Eng 43(4):427–445

    Article  Google Scholar 

  • Hassanpour J, Rostami J, Zhao J (2011) A new hard rock TBM performance prediction model for project planning. Tunn Undergr Space Technol 26(5):595–603

    Article  Google Scholar 

  • Hassanpour, J. (2009). Investigation of the effect of engineering geological parameters on TBM performance and modifications to existing prediction models (Doctoral dissertation, Ph. D. Thesis, Tarbiat Modares University, Tehran, Iran).

  • Hosseini SM, Ataei M, Khalokakaei R, Mikaeil R, Haghshenas SS (2020) Study of the effect of the cooling and lubricant fluid on the cutting performance of dimension stone through artificial intelligence models. Eng Sci Technol Int J 23(1):71–81

    Google Scholar 

  • Hosseini, S. M., Ataei, M., Khalokakaei, R., Mikaeil, R., & Haghshenas, S. S. (2019). Investigating the Role of the Cooling and Lubricant Fluids on the Performance of Cutting Disks (Case Study: Hard Rocks). Rudarsko-geološko-naftni zbornik, 34(2).

  • Hosseinzadeh A, Moeinaddini A, Ghasemzadeh A (2021) Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model. J Safety Res 77:151–160

    Article  Google Scholar 

  • Howarth, D. F. (1986). Review of rock drillability and borability assessment methods. Transactions-Institute of Mining & Metallurgy, Section A, 95(October).

  • Hughes HM (1986) The relative cuttability of coal-measures stone. Min Sci Technol 3(2):95–109

    Article  Google Scholar 

  • Innaurato, N., Mancini, A., Rondena, E., & Zaninetti, A. (1991, January). Forecasting and effective TBM performances in a rapid excavation of a tunnel in Italy. In 7th ISRM Congress. International Society for Rock Mechanics and Rock Engineering.

  • Ivakhnenko AG (1971) Polynomial Theory of Complex Systems. IEEE Trans Syst Man Cybern SMC 1:364–378

    Article  Google Scholar 

  • Jalali SME, Naghadehi MZ (2009) An analytical pre-feasibility study to generate rotary-percussive concept in hard rock TBMs. J Geol Mining Res 1(6):140–148

    Google Scholar 

  • Jalali SME, Naghadehi MZ (2013) Development of a new laboratory apparatus for the examination of the rotary-percussive penetration in tunnel boring machines. Tunn Undergr Space Technol 33:88–97

    Article  Google Scholar 

  • Kahraman S (2002) Correlation of TBM and drilling machine performances with rock brittleness. Eng Geol 65(4):269–283

    Article  Google Scholar 

  • Karami M, Zare S, Rostami J (2021) Introducing an empirical model for prediction of disc cutter life for TBM application in jointed rocks: case study, Kerman water conveyance tunnel. Bull Eng Geol Env 80(5):3853–3870

    Article  Google Scholar 

  • Khademi A, Behfarnia K, Kalman Šipoš T, Miličević I (2021) The use of machine learning models in estimating the compressive strength of recycled brick aggregate concrete. Comput Eng Phys Model 4(4):1–25

    Google Scholar 

  • Koopialipoor M, Tootoonchi H, Armaghani DJ, Mohamad ET, Hedayat A (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Env 78(8):6347–6360

    Article  Google Scholar 

  • Koopialipoor M, Fahimifar A, Ghaleini EN, Momenzadeh M, Armaghani DJ (2020) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers 36(1):345–357

    Article  Google Scholar 

  • Looney CG (1996) Advances in feedforward neural networks: Demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8:211–226

    Article  Google Scholar 

  • Lu S, Liu Y (2018) Evaluation system for the sustainable development of urban transportation and ecological environment based on SVM. J Intell Fuzzy Syst 34(2):831–838

  • Mafarja M, Aljarah I, Faris H, Hammouri AI, Ala’M, A. Z., & Mirjalili, S. (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mansouri M, Moomiv H (2010) Influence of rock mass properties on TBM penetration rate in Karaj-Tehran water conveyance tunnel. J Geol Mining Res 2(5):114–121

    Google Scholar 

  • Meharie MG, Shaik N (2020) Predicting highway construction costs: comparison of the performance of random forest, neural network and support vector machine models. J Soft Comput Civil Eng 4(2):103–112

    Google Scholar 

  • Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A (2021) Grasshopper optimization algorithm: theory, variants, and applications. IEEE Access 9:50001–50024

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mikaeil R, Haghshenas SS, Shirvand Y, Hasanluy MV, Roshanaei V (2016) Risk assessment of geological hazards in a tunneling project using harmony search algorithm (case study: Ardabil-Mianeh railway tunnel). Civil Engineering Journal 2(10):546–554

    Article  Google Scholar 

  • Mikaeil R, Haghshenas SS, Hoseinie SH (2018a) Rock penetrability classification using artificial bee colony (ABC) algorithm and self-organizing map. Geotech Geol Eng 36(2):1309–1318

    Google Scholar 

  • Mikaeil R, Haghshenas SS, Ozcelik Y, Gharehgheshlagh HH (2018b) Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotech Geol Eng 36(6):3779–3791

    Article  Google Scholar 

  • Mikaeil R, Bakhshinezhad H, Haghshenas SS, Ataei M (2019a) Stability analysis of tunnel support systems using numerical and intelligent simulations (case study: Kouhin Tunnel of Qazvin-Rasht Railway). Rudarsko-Geološko-Naftni Zbornik 34(2):1–10

    Article  Google Scholar 

  • Mikaeil R, Haghshenas SS, Sedaghati Z (2019b) Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: The second part of Emamzade Hashem tunnel). Nat Hazards 97(3):1099–1113

    Article  Google Scholar 

  • Mikaeil R, Beigmohammadi M, Bakhtavar E, Haghshenas SS (2019c) Assessment of risks of tunneling project in Iran using artificial bee colony algorithm. SN Applied Sciences 1(12):1–9

    Article  Google Scholar 

  • Mikaeil R, Esmaeilzade A, Haghshenas SS, Ataei M, Hajizadehigdir S, Jafarpour A, Geem ZW (2022a) Evaluation of dimension stone according to resistance to freeze-thaw cycling to use in cold regions. Journal of Soft Computing in Civil Engineering 6(1):88–109

    Google Scholar 

  • Mikaeil R, Mokhtarian M, Shaffiee Haghshenas S, Careddu N, Alipour A (2022b) Assessing the system vibration of circular sawing machine in carbonate rock sawing process using experimental study and machine learning. Geotech Geol Eng 40(1):103–119

    Article  Google Scholar 

  • Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering 16:213–219

    Article  Google Scholar 

  • Naderpour H, Nagai K, Fakharian P, Haji M (2019) Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 215:69–84

    Article  Google Scholar 

  • Naderpour H, Eidgahee DR, Fakharian P, Rafiean AH, Kalantari SM (2020) A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Engineering Science and Technology, an International Journal 23(2):382–391

    Article  Google Scholar 

  • Nelson, P. (1983). Tunnel boring machine performance in sedimentary rock. Cornell University.

  • Noori AM, Mikaeil R, Mokhtarian M, Haghshenas SS, Foroughi M (2020) Feasibility of intelligent models for prediction of utilization factor of TBM. Geotech Geol Eng 38(3):3125–3143

    Article  Google Scholar 

  • Ozdemir, L., & Wang, F. D. (1997). Mechanical tunnel boring prediction and machine design. Annual report.

  • Pan JS, Wang X, Chu SC, Nguyen TT (2020) A multi-group grasshopper optimisation algorithm for application in capacitated vehicle routing problem. Data Sci Pattern Recog 4(1):41–56

    Google Scholar 

  • Pirouz B, Shaffiee Haghshenas S, Shaffiee Haghshenas S, Piro P (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12(6):2427

    Article  Google Scholar 

  • Pirra M, Diana M (2019) A study of tour-based mode choice based on a support vector machine classifier. Transp Plan Technol 42(1):23–36

    Article  Google Scholar 

  • Plakandaras V, Papadimitriou T, Gogas P (2019) Forecasting transportation demand for the US market. Transportation Research Part a: Policy and Practice 126:195–214

    Google Scholar 

  • Rad, M.Y.; Haghshenas, S.S.; Kanafi, P.R.; Haghshenas, S.S. Analysis of protection of body slope in the rockfill reservoir dams on the basis of fuzzy logic. In Proceedings of the 4th International Joint Conference on Computational Intelligence, Barcelona, Spain, 5–7 October 2012.

  • Rad, M.Y.; Haghshenas, S.S.; Haghshenas, S.S. Mechanostratigraphy of cretaceous rocks by fuzzy logic in East Arak, Iran. In Proceedings of the 4th International Workshop on Computer Science and Engineering—Summer, Dubai, UAE, 22–23 August 2014.

  • Rahul AK, Shivhare N, Kumar S, Dwivedi SB, Dikshit PKS (2021) Modelling of daily suspended sediment concentration using FFBPNN and SVM algorithms. J Soft Comp Civil Eng 5(2):120–134

    Google Scholar 

  • Ramezanzadeh, A. (2005). Performance analysis and development of new models for performance prediction of hard rock TBMs in rock mass (Doctoral dissertation, Lyon, INSA).

  • 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(2):105–127

    Article  Google Scholar 

  • Rostami J (2008) Hard rock TBM cutterhead modeling for design and performance prediction. Geomechanik Und Tunnelbau: Geomechanik Und Tunnelbau 1(1):18–28

    Article  Google Scholar 

  • Rostami, J. (1993, June). A new model for performance prediction of hard rock TBMs. In Proceedings/1993 Rapid Excavation And Tunneling Conference.

  • 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 (Doctoral dissertation, Colorado School of Mines).

  • Roxborough, F. F., & Phillips, H. R. (1975, December). Rock excavation by disc cutter. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 12, No. 12, pp. 361–366). Pergamon.

  • Salemi A, Mikaeil R, Haghshenas SS (2018) Integration of finite difference method and genetic algorithm to seismic analysis of circular shallow tunnels (Case study: Tabriz urban railway tunnels). KSCE J Civ Eng 22(5):1978–1990

    Article  Google Scholar 

  • 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 Mining Mineral Eng 4(3):249–264

    Article  Google Scholar 

  • Sanio HP (1985) Prediction of the performance of disc cutters in anisotropic rock. Int J Rock Mechanics and Mining Sciences & Geomechanics Abstracts 22(3):153–161

    Article  Google Scholar 

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  • Sato K (1991) Prediction of disc cutter performance using a circular rock cutting rig. In Proceedings of the First International Symposium on Mine Mechanization, Golden, Colorado 1:1–31

    Google Scholar 

  • Saxena A (2019) A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm. Expert Syst Appl 132:166–188

    Article  Google Scholar 

  • Shaffiee Haghshenas S, Pirouz B, Shaffiee Haghshenas S, Pirouz B, Piro P, Na KS, Geem ZW (2020) Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of COVID-19 based on artificial intelligence applications. Int J Environ Res Public Health 17(10):3730

    Article  Google Scholar 

  • Shaghaghi S, Bonakdari H, Gholami A, Ebtehaj I, Zeinolabedini M (2017) Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design. Appl Math Comput 313:271–286

    Google Scholar 

  • Shao, C., Li, X., & Su, H. (2013, September). Performance prediction of hard rock TBM based on extreme learning machine. In International Conference on Intelligent Robotics and Applications (pp. 409–416). Springer, Berlin, Heidelberg.

  • Shi Y, Li Y, Fan J, Wang T, Yin T (2020) A novel network architecture of decision-making for self-driving vehicles based on long short-term memory and grasshopper optimization algorithm. IEEE Access 8:155429–155440

    Article  Google Scholar 

  • Snowdon, R. A., Ryley, M. D., & Temporal, J. (1982, June). A study of disc cutting in selected British rocks. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 19, No. 3, pp. 107–121). Pergamon.

  • Sundin, N. O., & Wänstedt, S. (1994). A boreability model for TBM's. In 1st North American Rock Mechanics Symposium. American Rock Mechanics Association.

  • Tang RX, Kulatilake PH, Yan EC, Cai JS (2020) Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. Bull Eng Geol Env 79(5):2235–2254

    Article  Google Scholar 

  • Xiao J (2019) SVM and KNN ensemble learning for traffic incident detection. Physica A 517:29–35

    Article  Google Scholar 

  • Xing Y, Kulatilake PHSW, Sandbak LA (2017) Rock mass stability investigation around tunnels in an underground mine in USA. Geotech Geol Eng 35(1):45–67

    Article  Google Scholar 

  • Xing Y, Kulatilake PHSW, Sandbak LA (2018) Effect of rock mass and discontinuity mechanical properties and delayed rock supporting on tunnel stability in an underground mine. Eng Geol 238:62–75

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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(4–5):808–814

    Article  Google Scholar 

  • Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30(4):207–222

    Article  Google Scholar 

  • Zeng J, Roussis PC, Mohammed AS, Maraveas C, Fatemi SA, Armaghani DJ, Asteris PG (2021) Prediction of peak particle velocity caused by blasting through the combinations of boosted-CHAID and SVM models with various kernels. Appl Sci 11(8):3705

    Article  Google Scholar 

  • Zhou J, Qiu Y, Zhu S, Armaghani DJ, Li C, Nguyen H, Yagiz S (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

We would like to express our deepest thanks to Professor Mahdi Ghaem for his excellent advice.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammadreza Akbarzadeh, Seyed Mohammad Esmaeil Jalali & Shokrollah Zare

  2. Department of Civil Engineering, University of Calabria, 87036, Rende, Italy

    Sina Shaffiee Haghshenas

  3. Department of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran

    Reza Mikaeil

Authors
  1. Mohammadreza Akbarzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sina Shaffiee Haghshenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Mohammad Esmaeil Jalali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shokrollah Zare
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Reza Mikaeil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sina Shaffiee Haghshenas.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest or financial conflicts to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akbarzadeh, M., Shaffiee Haghshenas, S., Jalali, S.M.E. et al. Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification. Geotech Geol Eng 40, 4685–4703 (2022). https://doi.org/10.1007/s10706-022-02178-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-022-02178-7

Keywords

Search

Navigation