Prediction of tunnel boring machine penetration using Group Method of Data Handling (GMDH) neural network

Document Type : research - paper

Authors

1 Assistant Professor, Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

2 Department of Mechanical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

3 M.Sc. Student, Department of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Predicting penetration rate of tunnel boring machines (TBM) is a decisive factor in scheduling and budgeting of tunnelling projects. This paper is aimed at predicting the TBM penetration rate (ROP) in the southern lot of Kerman water conveyance tunnel by means of Group method of Data Handling (GMDH) neural network. Having compiled the database using the geomechanical data of rock mass and machine performance data, correlations between ten various parameters were explored and two linear and nonlinear multivariate regression equations for the penetration rate were set up. Multi-objective Genetic Algorithm in the form of bi-objective optimization was applied for designing the optimal structure of the network and the dataset was randomly divided into training subset (70% of the total data) and test subset (the remaining 30%) as two objective functions. A multi-layered polynomial penetration rate function in terms of the parameters having the strongest correlation with ROP, i.e. compressive strength of the rock mass, quartz content, the angle between plane of weakness and TBM-driven direction, and the average force acting on the single cutter was obtained. Application of the rock mass compressive strength led to reducing the number of involving parameters and making the prediction model simpler. The comparison of the observed and predicted values showed high determination coefficient (R2) of 0.81 (R2=0.6 for nonlinear multivariate regression) which reveals high prediction capability of the proposed GMDH model. Unlike other neural network prediction models which produce their outputs as a “black box”, the suggested ROP GMDH model was expressed as a recurrent polynomial function in terms of the inputs. This outstanding feature of the GMDH model enables the proposed prediction model to be used in other projects as well as future research.

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References

H.P. Sanio, 1985; “Prediction of the performance of disc cutters in anisotropic rock”, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., vol. 22 (3), pp. 153–161.## O.T. Blindheim, 1979; “Boreability predictions for tunneling Ph.D. Thesis”, Department of Geological Engineering. The Norwegian Institute of Technology. ## J. Rostami, 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. ## A. Bruland, 1998; “Hard rock tunnel boring Ph.D. Thesis”, Norwegian University of Science and Technology, Trondheim. ## N. Barton, 2000; “TBM Tunnelling in Jointed and Faulted Rock”, Balkema, Rotterdam. ## J. Hassanpour; J. Rostami; M. Khamehchiyan, A. Bruland, H.R. Tavakoli, 2010; “TBM performance analysis in pyroclastic rocks: a case history of karaj water conveyance tunnel”, Rock Mech. Rock Eng., vol. 43 (4), pp. 427–445. ## J. Khademi Hamidi; K. Shahriar; B. Rezai, J. Rostami, 2010; “Performance prediction of hard rock TBM using rock mass rating (RMR) system”, Tunn. Undergr. Space Technol., vol. 25(4), pp. 333–345. ## E. Farrokh; J. Rostami; C. Laughton, 2012; “Study of various models for estimation of penetration rate of hard rock TBMs”, Tunn. Undergr. Space Technol., vol. 30, pp.110–123. ## A. Benato; P. Oreste, 2015; “Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics”, Int. J. Rock Mech. Min. Sci., vol. 74, pp. 119–127. ## O. Frough; S.R. Torabi; S. Yagiz, 2015; “Application of RMR for estimating rock-mass-related TBM utilization and performance parameters: a case study”, Rock Mech. Rock Eng., vol. 48 (3), pp. 1305–1312. ## A. Salimi; J. Rostami; C. Moormann; A. Delisio, 2016; “Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs”, Tunn. Undergr. Space Technol., vol. 58, pp. 236–246. ## G. Armettia; M.R. Migliazzab; F. Ferraric; A. Bertid; P. Padovesed, 2018; “Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel”, Tunn. Undergr. Space Technol., vol. 77, pp. 115–126. ## M. Entacher; J. Rostami, 2019; “TBM performance prediction model with a linear base function and adjustment factors obtained from rock cutting and indentation tests”, Tunnell. Undergr. Space Technol., vol. 93, no.103085. ## H. Xu; Q. Gong ; J. Lu; L. Yin; F. Yang, 2021; “Setting up simple estimating equations of TBM penetration rate using rock mass classification parameters”, Tunnell. Undergr. Space Technol., vol. 115, no. 104065## M. Alvarez Grima; P.A. Bruines; P.N.W. Verhoef, 2000; “Modeling tunnel boring machine performance by neuro-fuzzy methods”, Tunnell. Undergr. Space Technol., vol. 15 (3), pp. 259–269. ## A.G. Benardos; D.C. Kaliampakos, 2004; “Modelling TBM performance with artificial neural networks”, Tunnell. Undergr. Space Technol., vol. 19, pp. 597–605. ## S. Yagiz; C. Gokceoglu; E. Sezer; S. Iplikci, 2009; “Application of two nonlinear prediction tools to the estimation of tunnel boring machine performance”, Eng. Appl. Artif. Intell., vol. 22, pp. 808–814. ## E. Ghasemi E; S. Yagiz; M. Ataei, 2014; “Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic”, Bull. Eng. Geol. Environ., vol. 73, pp. 23–35. ## S. Mahdevari S; K. Shahriar; S. Yagiz; M.A. Shirazi, 2014; “A support vector regression model for predicting tunnel boring machine penetration rates”, Int. J. Rock Mech. Min. Sci., vol. 72, pp. 214–229. ## S. Yagiz ; H. Karahan, 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., vol. 80, pp. 308–315. ## D.J. Armaghani; E.T. Mohamad; M.S. Narayanasamy et al., 2017; “Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition”, Tunn. Undergr. Sp. Technol., vol. 63, pp. 29–43. ## J. Zhou; B. Yazdani Bejarbaneh; D.J. Armaghani; M.M. Tahir, 2020; “Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques”, Bull. Eng. Geol. Environ., vol. 79, pp. 2069–2084. ## F. Shangxina; C. Zuyub; L. Huac et al., 2021; “Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning”, Tunn. Undergr. Sp. Technol., vol. 110, no. 103636. ## V.T. Minh; D. Katushin; M. Antonov; R. Veinthal, 2017; “Regression models and fuzzy logic prediction of TBM penetration rate”, Open Eng., vol. 7 (1), pp. 60–68. ## S. Yagiz; H. Karahan, 2011; “Prediction of hard rock TBM penetration rate using particle swarm optimization”, Int. J. Rock Mech. Min. Sci., vol. 48, pp. 427–433. ## E. Sfidari et al., 2018; “Prediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin”, Geopersia, vol. 8(1), pp. 43-60. ## A. Jamali et al., 2015; “Reliability-based optimal controller design for systems with probabilistic uncertain parameters using fuzzy limit state function”, Journal of Vibration and Control, vol. 21(7), pp. 1413-1429##. A. Jamali et al., 2013; “Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA)”, Engineering Applications of Artificial Intelligence, vol. 26(2), pp. 714-723##. B. Ahmadi; N. Nariman-zadeh; A. Jamali, 2017; “Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory”, Engineering Optimization, vol. 49(6), pp. 932-947. ## M. Parsa; E.J.M. Carranza, B. Ahmadi, 2022; “Deep GMDH Neural Networks for Predictive Mapping of Mineral Prospectivity in Terrains Hosting Few but Large Mineral Deposits”, Natural Resources Research, vol. 31(1), pp. 37-50. ## S.J. Farlow, 1984; “The GMDH algorithm”, Self-organizing methods in modeling” GMDH type algorithms, vol. 54, p. 350. ## N. Nariman-Zadeh, et al., 2005; “Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process”, Journal of Materials Processing Technology, vol. 164-165, pp. 1561-1571. ## N. Nariman-Zadeh; A. Darvizeh; G. Ahmad-Zadeh, 2003; “Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 217(6): pp. 779-790. ## مهندسان مشاور ساحل امید ایرانیان- زیستاب. 1396 . گزارش زمین‌شناسی مهندسی تونل انتقال آب کرمان. ## پورهاشمی، سیدمهدی؛ آهنگری، کاوه؛ حسن‌پور، جعفر، افتخاری، سید مصلح؛ 1400؛ «تحلیل نرخ نفوذ ماشین حفار تمام مقطع در شرایط سنگسایی»، نشریه علمی مهندسی معدن، دوره 16، شماره 52 ، صفحه 78 تا 88. A. Bruland, 1999; “Hard Rock Tunnel Boring: Advance Rate and Cutter Wear”, Trondheim, Norway: Norwegian Institute of Technology (NTNU). ## A. Salimi; J. Rostami; C. Moormann; A. Delisio, 2019; “Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms”, Tunn. Undergr. Space Technol., vol. 92, 103046. ## E. Hoek; E.T. Brown, 2019; “The Hoek-Brown failure criterion and GSI-2018 edition “, J. Rock Mech. Geotech. Eng., vol. 11(3), pp. 445-463. ##
Journal of Mining Engineering
Volume 18, Issue 59
May 2023
Pages 75-85

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History

  • Receive Date: 03 November 2022
  • Revise Date: 22 April 2023
  • Accept Date: 24 April 2023

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