پیش‌بینی نرخ نفوذ ماشین تونلزنی با استفاده از دسته بندی گروهی داده‌های شبکه عصبی

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. ##