Skip to main content
Springer Nature Link
Log in

A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Blindheim OT (2004) TBM performance prediction models. Tunnels & Tunnelling International 36 (12):23,25–27

  2. Kahraman S (2002) Correlation of TBM and drilling machine performances with rock brittleness. Eng Geol 65(4):269–283. https://doi.org/10.1016/S0013-7952(01)00137-5

    Article  Google Scholar 

  3. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Space Technol 81:112–120. https://doi.org/10.1016/j.tust.2018.07.023

    Article  Google Scholar 

  4. Liu BL, Yang HQ, Karekal S (2020) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng 53(2):799–813. https://doi.org/10.1007/s00603-019-01947-w

    Article  Google Scholar 

  5. Hassanpour J, Vanani AAG, Rostami J, Cheshomi A (2016) Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2). Tunnelling Underground Space Technol Incorporating Trenchless Technol Res 52:147–156. https://doi.org/10.1016/j.tust.2015.12.006

    Article  Google Scholar 

  6. O'Rourke J, Springer J, Coudray S (1994) Geotechnical parameters and tunnel boring machine performance at Goodwin tunnel, California. In: 1st North American rock mechanics symposium, Austin, Texas, USA, 1–3 June 1994.

  7. 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. https://doi.org/10.1016/j.tust.2010.01.008

    Article  Google Scholar 

  8. Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. Int J Rock Mech Miningences Geomech Abstracts 12(12):361–366

    Article  Google Scholar 

  9. Bieniawski ZT, Celada B, Galera JM (2007) TBM excavability: prediction and machine-rock interaction. In: Proceedings—Rapid Excavation And Tunneling Conference, Toronto, Ontario, Canada, 10–13 June 2007. pp 1118–1130

  10. 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 

  11. Bamford W (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Australian tunnelling conference, sydney, 5th 1984.

  12. Mansouri M, Torabi SR, Forough O, Goshtasbi K (2008) Influence of rock mass properties on TBM penetration rate in Karaj-Tehran water conveyance tunnel. Am J Eng Appl ences 2(3):114–121. https://doi.org/10.3844/ajeassp.2010.540.544

    Article  Google Scholar 

  13. Rostami J, Ozdemir L (1993) New model for performance production of hard rock TBMs. In: Proceedings rapid excavation & tunneling conference, Boston, Massachusetts, USA, 13–17 June 1993. pp 793–809

  14. Bruland A (1998) Hard Rock Tunnel Boring. PhD dissertation. Norwegian University of Sciences and Technology, Trondheim

  15. Barton N (1999) TBM performance estimation in rock using Q(TBM). Tunnel Tunnelling Int 31(9):30–34

    Google Scholar 

  16. Palmstrom A (1995) RMi-a rock mass characterization system for rock engineering purposes. PhD dissertation. University of Oslo, Norway

  17. Bieniawski Z, Celada B, Galera J, MH A (2006) Rock mass excavability (RME) index. In: ITA World Tunnel Congress, Korea, 2006.

  18. Blindheim O (2005) A critique of QTBM. Tunnels Tunnelling Int 37(6):32–35

    Google Scholar 

  19. Palmstrom A, Broch E (2006) Use and misuse of rock mass classification systems with particular reference to the Q-system. Tunn Undergr Space Technol 21(6):575–593. https://doi.org/10.1016/j.tust.2005.10.005

    Article  Google Scholar 

  20. Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46(1):8–18. https://doi.org/10.1016/j.ijrmms.2008.03.003

    Article  Google Scholar 

  21. Wang R, Hu ZP, Zhang D, Wang QY (2017) Propagation of the stress wave through the filled joint with linear viscoelastic deformation behavior using time-domain recursive method. Rock Mech Rock Eng 50(12):3197–3207. https://doi.org/10.1007/s00603-017-1301-4

    Article  Google Scholar 

  22. Armaghani DJ, Faradonbeh RS, Momeni E, Fahimifar A, Tahir MM (2018) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Eng Comput 34(1):129–141. https://doi.org/10.1007/s00366-017-0526-x

    Article  Google Scholar 

  23. Adoko AC, Gokceoglu C, Yagiz S (2017) Bayesian prediction of TBM penetration rate in rock mass. Eng Geol 52:147–156. https://doi.org/10.1016/j.enggeo.2017.06.014

    Article  Google Scholar 

  24. Shao C, Li X, Su H (2013) Performance prediction of hard rock TBM based on extreme learning machine. Int Conf Intell Robot Appl Berlin Heidelberg 2013:409–416

    Google Scholar 

  25. Milovančević M, Marinović JS, Nikolić J, Kitić A, Shariati M, Trung NT, Wakil K, Khorami M (2019) UML diagrams for dynamical monitoring of rail vehicles. Phys A 531:121169. https://doi.org/10.1016/j.physa.2019.121169

    Article  Google Scholar 

  26. 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. https://doi.org/10.1016/j.engappai.2009.03.007

    Article  Google Scholar 

  27. Xu SX, He Y, Zhu KJ, Liu T, Li Y (2008) A PSO–ANN Integrated Model of Optimizing Cut-Off Grade and Grade of Crude Ore. In: Fourth international conference on natural computation, 8–20 Oct 2008.

  28. Armaghani Danial J, Mirzaei F, Shariati M, Trung Nguyen T, Shariati M, Trnavac D (2020) Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech Eng 20(3):191–205. https://doi.org/10.12989/GAE.2020.20.3.191

    Article  Google Scholar 

  29. Shariati M, Mafipour MS, Mehrabi P, Bahadori A, Zandi Y, Salih MN, Nguyen H, Dou J, Song X, Poi-Ngian S (2019) Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Appl Sci 9(24):5534

    Article  Google Scholar 

  30. Shariati M, Mafipour Mohammad S, Mehrabi P, Ahmadi M, Wakil K, Trung Nguyen T, Toghroli A (2020) Prediction of concrete strength in presence of furnace slag and fly ash using hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm). Smart Structures and Systems 25(2):183–195. https://doi.org/https://doi.org/10.12989/SSS.2020.25.2.183

  31. Murlidhar BR, Armaghani DJ, Mohamad ET, Changthan S (2018) Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Construction Res 2(3):1–12. https://doi.org/10.18063/scr.v2i3.397

    Article  Google Scholar 

  32. Chen W, Sarir P, Bui X-N, Nguyen H, Tahir M, Armaghani DJ (2019) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Engineering with Computers:1–15. https://doi.org/https://doi.org/10.1007/s00366-019-00752-x

  33. Armaghani Danial J, Mirzaei F, Toghroli A, Shariati A (2020) Indirect measure of shear strength parameters of fiber-reinforced sandy soil using laboratory tests and intelligent systems. Geomech Eng 22(5):397–414. https://doi.org/10.12989/GAE.2020.22.5.397

    Article  Google Scholar 

  34. 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. https://doi.org/10.1016/j.tust.2016.12.009

    Article  Google Scholar 

  35. Majid M, Mojtaba K, Rupp C, S.-K. TD, Mehrdad S (2018) Power production prediction of wind turbines using fusion of MLP and ANFIS networks. IET Renew Power Generation 12(9):1025-1033. https://doi.org/https://doi.org/10.1049/iet-rpg.2017.0736

  36. Shariati M, Mafipour Mohammad S, Haido James H, Yousif Salim T, Toghroli A, Trung Nguyen T, Shariati A (2020) Identification of the most influencing parameters on the properties of corroded concrete beams using an adaptive neuro-fuzzy inference system (ANFIS). Steel and Composite Struct 34(1):155–170. https://doi.org/10.12989/SCS.2020.34.1.155

    Article  Google Scholar 

  37. Safa M, Sari PA, Shariati M, Suhatril M, Trung NT, Wakil K, Khorami M (2020) Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes. Phys A 550:124046. https://doi.org/10.1016/j.physa.2019.124046

    Article  Google Scholar 

  38. Oraee K, Khorami MT, Hosseini N (2012) Prediction of the penetration rate of tbm using adaptive neuro fuzzy inference system (ANFIS). Proc SME Annual Meeting Exhibit Mine Market Now It’s Global Seattle WA USA 2012:297–302

    Google Scholar 

  39. Shariati M, Mafipour Mohammad S, Mehrabi P, Zandi Y, Dehghani D, Bahadori A, Shariati A, Trung Nguyen T, Salih Musab NA, Poi-Ngian S (2019) Application of extreme learning machine (ELM) and genetic programming (GP) to design steel-concrete composite floor systems at elevated temperatures. Steel Composite Struct 33(3):319–332. https://doi.org/10.12989/SCS.2019.33.3.319

    Article  Google Scholar 

  40. Shariati M, Mafipour MS, Mehrabi P, Shariati A, Toghroli A, Trung NT, Salih MNA (2020) A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Eng Comput. https://doi.org/10.1007/s00366-019-00930-x

    Article  Google Scholar 

  41. Shariati M, Trung NT, Wakil K, Mehrabi P, Safa M, Khorami M (2019) Moment-rotation estimation of steel rack connection using extreme learning machine. Steel Composite Struct 31(5):427–435. https://doi.org/10.12989/scs.2019.31.5.427

    Article  Google Scholar 

  42. Shao C, Li X, Su H Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine. In, Berlin, Heidelberg, 2013. Intelligent Robotics and Applications. Springer Berlin Heidelberg, pp 409-416

  43. Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31. https://doi.org/10.1007/s00366-016-0453-2

    Article  Google Scholar 

  44. Yu Z, Shi X, Zhou J, Rao D, Chen X, Dong W, Miao X, Ipangelwa T (2019) Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models. Eng Comput:1–16. https://doi.org/https://doi.org/10.1007/s00366-019-00868-0

  45. Chen W, Hasanipanah M, Rad HN, Armaghani DJ, Tahir MM (2019) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng Comput:1–17. https://doi.org/https://doi.org/10.1007/s00366-019-00895-x

  46. Mahdevari S, Shahriar K, Yagiz S, Akbarpour Shirazi M (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229. https://doi.org/10.1016/j.ijrmms.2014.09.012

    Article  Google Scholar 

  47. Sari PA, Suhatril M, Osman N, Mu’azu MA, Dehghani H, Sedghi Y, Safa M, Hasanipanah M, Wakil K, Khorami M, Djuric S (2019) An intelligent based-model role to simulate the factor of safe slope by support vector regression. Eng Comput 35(4):1521-1531. https://doi.org/https://doi.org/10.1007/s00366-018-0677-4

  48. Salimi A, Moormanna C, Singh TN, Jain P (2015) TBM performance prediction in rock tunneling using various artificial intelligence algorithms. In: Proceeding 11th Iranian and 2nd regional conference, Stuttgart, Germany, November 2015.

  49. Ge Y, Wang J, Li K (2013) Prediction of hard rock TBM penetration rate using least square support vector machine. IFAC Proc Volumes 46(13):347–352. https://doi.org/10.3182/20130708-3-CN-2036.00105

    Article  Google Scholar 

  50. Xu H, Zhou J, G Asteris P, Jahed Armaghani D, Tahir MM (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9(18):3715. Doi: https://doi.org/10.3390/app9183715

  51. Zhou J, Bejarbaneh BY, Armaghani DJ, Tahir M (2019) Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bull Eng Geol Environ:1–16. https://doi.org/https://doi.org/10.1007/s10064-019-01626-8

  52. Heidari AA, Mirvahabi SS, Homayouni S (2015) An effective hybrid support vector regression with chaos-embedded biogeography-based optimization strategy for prediction of earthquake-triggered slope deformations. Int Arch Photogramm 41 (W5):301-305. https://doi.org/https://doi.org/10.5194/isprsarchives-XL-1-W5-301-2015

  53. Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) SimpleMKL. J Mach Learn Res 9(3):2491–2521

    MathSciNet  MATH  Google Scholar 

  54. Vapnik V, Golowich SE, Smola AJ Support vector method for function approximation, regression estimation and signal processing. In: Neural information processing systems, 1996. pp 281–287

  55. Cortes C, Vapnik V (1995) Support-vector networks. Machine Learn 20(3):273–297. https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  56. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  57. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  58. Castro-Neto M, Jeong YS, Jeong MK, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173. https://doi.org/10.1016/j.eswa.2008.07.069

    Article  Google Scholar 

  59. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783–789. https://doi.org/10.1016/j.eswa.2008.07.069

    Article  Google Scholar 

  60. Xiao J, Wei C, Liu Y (2018) Speed estimation of traffic flow using multiple kernel support vector regression. Phys A 509:989–997. https://doi.org/10.1016/j.physa.2018.06.082

    Article  Google Scholar 

  61. Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Microsoft research technical report

  62. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  63. Muro C, Escobedo R, Spector L, Coppinger RP (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Proc 88(3):192–197. https://doi.org/10.1016/j.beproc.2011.09.006

    Article  Google Scholar 

  64. Mech DL (1999) Alpha status, dominance, and division of labor in wolf packs. Can J Zool 77(8):1196–1203. https://doi.org/10.1139/z99-099

    Article  Google Scholar 

  65. Shariati M, Mafipour MS, Ghahremani B, Azarhomayun F, Ahmadi M, Trung NT, Shariati A (2020) A novel hybrid extreme learning machine-grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng Comput:23. https://doi.org/https://doi.org/10.1007/s00366-020-01081-0

  66. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. https://doi.org/10.1109/TEVC.2008.919004

    Article  Google Scholar 

  67. Harandizadeh H, Armaghani DJ, Khari M (2019) A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Engineering With Computers:1–16. https://doi.org/https://doi.org/10.1007/s00366-019-00849-3

  68. Khamesi H, Torabi SR, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the karaj subway line 2 in Iran. J Comput Civ Eng 29(6):05014010. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000421

    Article  Google Scholar 

  69. Zhang H, Wang Y-j, Li Y-f (2009) SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence. Min Sci Technol 19(3):385–388. https://doi.org/10.1016/s1674-5264(09)60072-7

    Article  Google Scholar 

  70. Katebi J, Shoaei-parchin M, Shariati M, Trung NT, Khorami M (2019) Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures. Engineering with Computers:1–20

  71. Djema MA, Boudour M, Agbossou K, Cardenas A, Doumbia ML (2019) Adaptive direct power control based on ANN-GWO for grid interactive renewable energy systems with an improved synchronization technique. Int Trans Electr Energy Syst 29(3):15. https://doi.org/10.1002/etep.2766

    Article  Google Scholar 

  72. Dehghani M, Riahi-Madvar H, Hooshyaripor F, Mosavi A, Shamshirband S, Zavadskas EK, Chau KW (2019) Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2):20. https://doi.org/10.3390/en12020289

    Article  Google Scholar 

  73. Yesiloglu-Gultekin N, Gokceoglu C, Sezer EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci 62:113–122. https://doi.org/10.1016/j.ijrmms.2013.05.005

    Article  Google Scholar 

  74. Yagiz S, Sezer EA, 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 Meth Geomech 36(14):1636–1650. https://doi.org/10.1002/nag.1066

    Article  Google Scholar 

Download references

Acknowledgements

The financial support from the project supported by Graduate Research and Innovation Foundation of Chongqing, China (Grant No.CYB19015) and the fundamental research funds for the Natural Science Fund of China (No. 51879016) are greatly appreciated.

Author information

Authors and Affiliations

  1. State Key Laboratory of Coal Mine Disaster Dynamics and Control, School of Civil Engineering, Chongqing University, Chongqing, 400045, China

    Haiqing Yang, Zhihui Wang & Kanglei Song

  2. National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing, 400045, China

    Haiqing Yang, Zhihui Wang & Kanglei Song

Authors
  1. Haiqing Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhihui Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kanglei Song
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kanglei Song.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest and no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest that could be construed as influencing the review of this manuscript.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 88 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, H., Wang, Z. & Song, K. A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance. Engineering with Computers 38, 2469–2485 (2022). https://doi.org/10.1007/s00366-020-01217-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01217-2

Keywords