Skip to main content
SpringerLink
Log in

A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Frequent entry in the tunnel boring machine cutterhead for disc cutter wear inspection is a risky, time-consuming, and labor-intensive activity. Existing disc cutter wear prediction models mainly focus on cutter consumption before construction, and it is impossible to estimate the wear of a single cutter when they are applied to on-site construction. To solve this problem, this research presents a method for estimating the wear of each disc cutter on the cutterhead in real time by only using several monitored machine parameters. Firstly, a novel health index that can characterize the wear of each disc cutter is constructed, and the field parameters that have greater impact on the health index are selected. Then, the explicit mathematical expression between the selected parameters and the health index is established based on genetic expression programming. Finally, the on-site data collected from an Indian subway tunnel were used to validate the effectiveness and superiority of the proposed method. The results show that the proposed method can estimate the wear of each disc cutter in real time only by monitoring the rotational speed of cutterhead and tunneling speed. Its average accuracies on the validation set and test set are 90.6% and 85.9%, respectively. Compared with the ridge regression, decision tree, support vector regression and k-nearest neighbors, its accuracy on the test set is 13.0%, 12.7%, 11.2%, and 15.4% higher, respectively. Therefore, the proposed method can greatly reduce the cost for cutter inspection, and the explicit model can be easily deployed to the construction site.

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

Similar content being viewed by others

References

  1. Wei M, Wang ZL, Wang XY, Peng JL, Song Y (2021) Prediction of TBM penetration rate based on Monte Carlo-BP neural network. Neural Comput Appl 33(1):603–611

    Article  Google Scholar 

  2. Yu HG, Tao JF, Huang S, Qin CJ, Xiao DY, Liu CL (2021) A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead. Automat Constr 124:103603

    Article  Google Scholar 

  3. Qin CJ, Shi G, Tao JF, Yu HG, Jin YR, Xiao DY, Zhang ZN, Liu CL (2022) An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine. Mech Syst Signal Pr 175:109148

    Article  Google Scholar 

  4. Huang ZW, Zhu JM, Lei JT, Li XR, Tian FQ (2019) Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf 31(4):953–966

    Article  Google Scholar 

  5. Qin CJ, Xiao DY, Tao JF, Yu HG, Jin YR, Sun YX, Liu CL (2022) Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement 194:111090

    Article  Google Scholar 

  6. Tao JF, Qin CJ, Liu CL (2019) A synchroextracting-based method for early chatter identification of robotic drilling process. Int J Adv Manuf Technol 100(1–4):273–285

    Article  Google Scholar 

  7. Zerehsaz Y, Shao C, Jin JH (2019) Tool wear monitoring in ultrasonic welding using high-order decomposition. J Intell Manuf 30(2):657–669

    Article  Google Scholar 

  8. Hassanpour J, Rostami J, Azali ST, Zhao J (2014) Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel. Iran Tunn Undergr Space Technol 43(7):222–231

    Article  Google Scholar 

  9. Frenzel C (2011) Disc cutter wear phenomenology and their implications on disc cutter consumption for TBM. In: 45th US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association

  10. Sun ZC, Zhao HL, Hong KR, Chen K, Zhou JJ, Li FY, Zhang N, Song FL, Yang YD, He RY (2019) A practical TBM cutter wear prediction model for disc cutter life androck wear ability. Tunn Undergr Space Technol 85:92–99

    Article  Google Scholar 

  11. Liu QS, Liu JP, Pan YC, Zhang XP, Peng XX, Gong QM, Du LJ (2017) A wear rule and cutter life prediction model of a 20-in. TBM cutter for granite: a case study of a water conveyance tunnel in China. Rock Mech Rock Eng 50(5):1303–1320

    Article  Google Scholar 

  12. Liu BL, Yang HQ, Karekal S (2021) Reliability analysis of TBM disc cutters under different conditions. Undergr Space 6(2):142–152

    Article  Google Scholar 

  13. Rong XN, Lu H, Wang MY, Wen Z, Rong XL (2019) Cutter wear evaluation from operational parameters in EPB tunneling of Chengdu Metro. Tunn Undergr Space Technol 93:103043

    Article  Google Scholar 

  14. Hassanpour J (2018) Development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks. Tunn Undergr Space Technol 75:90–99

    Article  Google Scholar 

  15. Wang F, Men CH, Kong XW, Meng LX (2019) Optimum design and application research of eddy current sensor for measurement of TBM disc cutter wear. Sensors 19(19):4230

    Article  Google Scholar 

  16. Lan H, Xia YM, Ji ZY, Fu J, Miao B (2019) Online monitoring device of disc cutter wear-design and field test. Tunn Undergr Space Technol 89:284–294

    Article  Google Scholar 

  17. Shinouda MM, Gwildis UG, Wang P, Hodder W, Redmond S, Romero V (2011) Cutterhead maintenance for EPB tunnel boring machines. In: Proceedings rapid excavation and tunneling conference, San Francisco, CA

  18. Farrokh E, Kim DY (2018) A discussion on hard rock TBM cutter wear and cutterhead intervention interval length evaluation. Tunn Undergr Space Technol 81:336–357

    Article  Google Scholar 

  19. Ko TY, Kim TK, Son YJ, Jeon S (2016) Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling. Tunn Undergr Space Technol 57:99–111

    Article  Google Scholar 

  20. Er S, Tuğrul A (2016) Estimation of Cerchar abrasivity index of granitic rocks in Turkey by geological properties using regression analysis. Bull Eng Geol Environ 75(3):1325–1339

    Article  Google Scholar 

  21. Herrenknecht (2015) Disc Cutter Rotation Monitoring. https://www.herrenknecht.com/cn/suche/?tx_solr%5Bq%5D=dcrm

  22. Robbins (2017) Smartcutter. https://www.therobbinscompany.com/?s=Smartcutter

  23. Ehsan AG, Mooney MA, Frank G, Walter B, DiPonio MA (2013) Periodic inspection of gauge cutter wear on EPB TBMs using cone penetration testing. Tunn Undergr Space Technol 38:279–286

    Article  Google Scholar 

  24. Yu HG, Tao JF, Qin CJ, Liu MY, Xiao DY, Sun H, Liu CL (2022) A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mech Syst Signal Pr 165:108353

    Article  Google Scholar 

  25. Shi G, Qin C, Tao J, Liu C (2021) A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowl-Based Syst 228:107213

    Article  Google Scholar 

  26. 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 

  27. Jing LJ, Li JB, Yang C, Chen S, Zhang N, Peng XX (2019) A case study of TBM performance prediction using field tunnelling tests in limestone strata. Tunn Undergr Space Technol 83:364–372

    Article  Google Scholar 

  28. Hassanpour J, Rostami J, Zhao J, Azali ST (2015) TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran. Geomech Tunn 8(3):239–247

    Article  Google Scholar 

  29. Yu HG, Tao JF, Qin CJ, Xiao DY, Sun H, Liu CL (2021) Rock mass type prediction for tunnel boring machine using a novel semi-supervised method. Measurement 179:109545

    Article  Google Scholar 

  30. Amoun S, Sharifzadeh M, Shahriar K, Rostami J, Azali ST (2017) Evaluation of tool wear in EPB tunneling of Tehran Metro, Line 7 expansion. Tunn Undergr Space Technol 61:233–246

    Article  Google Scholar 

  31. Liu B, Wang RR, Guan ZD, Li JB, Xu ZH, Guo X, Wang YX (2019) Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data. Tunn Undergr Space Technol 91:102958

    Article  Google Scholar 

  32. Chao Q, Gao HH, Tao JF, Wang YH, Zhou J, Liu CL (2022) Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals. Sci China Technol Sc 65:470–480

    Article  Google Scholar 

  33. Jin Y, Qin C, Tao J, Liu C (2022) An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mech Syst Sig Process 165:108312

    Article  Google Scholar 

  34. Xiao DY, Qin CJ, Yu HG, Huang YX, Liu CL, Zhang JW (2021) Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals. Measurement 176:109186

    Article  Google Scholar 

  35. Ferreira C (2002) Gene expression programming: mathematical modeling by an artificial intelligence. Eng Appl Artif Intell 1(3):223–225

    Google Scholar 

  36. Nazari A (2020) Retraction Note to: Predicting the total specific pore volume of geopolymers produced from waste ashes by gene expression programming. Neural Comput Appl 32(8):17811

    Article  MathSciNet  Google Scholar 

  37. Janeiro FM, Santos JE, Ramos PM (2012) Gene expression programming in sensor characterization: Numerical results and experimental validation. IEEE Trans Instrum Meas 62(5):1373–1381

    Article  Google Scholar 

  38. Nce S, Bozda A, Fener M, Kahraman S (2019) Estimation of uniaxial compressive strength of pyroclastic rocks (Cappadocia, Turkey) by gene expression programming. Arab J Geosci 12(24):756

    Article  Google Scholar 

  39. Bingöl S, Kılıçgedik HY (2018) Application of gene expression programming in hot metal forming for intelligent manufacturing. Neural Comput Appl 30:937–945

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Armaghani D, Safari V, Fahimifar A, Amin MF, Monjezi M, Mohammadi MA (2018) Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Comput Appl 30(11):3523–3532

    Article  Google Scholar 

  42. Faradonbeh RS, Armaghani DJ, Monjezi M, Tonnizam E (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264

    Article  Google Scholar 

  43. Güllü H (2014) Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash. Eng Appl Artif Intell 35:143–157

    Article  Google Scholar 

  44. Frenzel C (2010). Verschleisskostenprognose für Schneidrollen bei maschinellen Tunnelvortrieben in Festgesteinen. Dr. Friedrich Pfeil

  45. Zhang QL, Liu ZY, Tan JR (2019) Prediction of geological conditions for a tunnel boring machine using big operational data. Autom Constr 100:73–83

    Article  Google Scholar 

  46. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems 2(13):87–129

    MathSciNet  MATH  Google Scholar 

  47. Yang Y, Li XY, Gao L, Shao XY (2013) A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming. J Netw Comput Appl 36(6):1540–1550

    Article  Google Scholar 

  48. Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38(4):4080–4087

    Article  Google Scholar 

  49. Ferreira C (2002) Gene expression programming in problem solving. Soft computing and industry. Springer, London, pp 635–653

    Chapter  Google Scholar 

  50. GEPSOFT (2013) GeneXproTools. https://www.gepsoft.com

Download references

Acknowledgements

This research was supported by Ministry of Education-China Mobile Research Foundation (Grant NO. MCM20180703), Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102), and State Key Laboratory of Mechanical System and Vibration (Grant No. MSVZD202103).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jianfeng Tao, Honggan Yu, Chengjin Qin, Hao Sun & Chengliang Liu

Authors
  1. Jianfeng Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Honggan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengjin Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengliang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honggan Yu.

Ethics declarations

Conflict of interest

There is no potential conflict of interest in our paper. And all authors have seen the manuscript and approved to submit to your journal.

Data availability

The data used in this paper to support the findings of this study are available from the corresponding author upon reasonable request.

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

Tao, J., Yu, H., Qin, C. et al. A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead. Neural Comput & Applic 34, 20231–20247 (2022). https://doi.org/10.1007/s00521-022-07597-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07597-4

Keywords

Search

Navigation