تحلیل احتمالاتی مدل‌های برآورد نرخ پیشروی ماشین حفر تمام مقطع تونل

[1]    F. Roxborough, H. Phillips, Rock excavation by disc cutter, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 12(12) (1975) 361-366.

[2]    H.P. Sanio, Prediction of the performance of disc cutters in anisotropic rock, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 22(3) (1985) 153-161.

[3]    K. Sato, Prediction of disc cutter performance using a circular rock cutting rig, in:  First International symposium on Mine Mechanization, Golden, Colorado, 1991.

[4]    R.A. Snowdon, M.D. Ryley, J. Temporal, A study of disc cutting in selected British rocks, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 19(3) (1982) 107-121.

[5]    R.J. Boyd, Hard rock continuous mining machine: Mobile Miner MM-120, in:  Rock excavation engineering seminar, Department of Mining and Metallurgical Engineering, University of Queelsland, 1986.

[6]    L. Ozdemir, Development of Theoretical Equations for Predicting Tunnel Borability, Colorado School of mines, Golden, Colorado, 1977.

[7]    P.C. Graham, Rock exploration for machine manufacturers, in:  Exploration for Rock Engineering, Balkema, Johannesburg, 1976, pp. 173-180.

[8]    I. Farmer, N. Glossop, Mechanics of disc cutter advance, Tunnels and Tunnelling, 12(6) (1980) 22-25.

[9]    P. Nelson, A. Ingraffea, T. Rourke, TBM performance prediction using rock fracture parameters, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 22(3) (1985) 189-192.

[10]   W. Bamford, Rock test indices are being successfully correlated with tunnel boring machine performance, in:  Fifth Australian Tunnelling Conference: State of the Art in Underground Development and Construction, 1984.

[11]   M. Grima, M. Alvarez, P. A. Bruines, P. Verhoef, Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunnelling and Underground Space Technology, 15(3) (2000) 259-269.

[12]   D.J. Armaghan, E.T. Mohamad, M.S. Narayansamy, N. Narita, S. Yagiz, Development of hybrid intelligent models for predicting TBM advance rate in hard rock condition, Tunnelling and Underground Space Technology, 63 (2017) 29-43.

[13]   R. Mikaeil, M.Z. Naghadehi, F. Sereshki, Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions, Tunnelling and Underground Space Technology, 24(5) (2009) 500-505.

[14]   O.T. Blindheim, Boreability predictions for tunneling The Norwegian Institute of Technology, 1979.

[15]   A. Ramezanzadeh, Perforamnce analysis and development of new models for performance prediction of hard rock TBM in rock mass, Ph.D Thesis, INSA, Lyon, (2005).

[16]   J. Hassanpour, J. Rostami, J. Zhao, A new hard rock TBM performance prediction model for project planning, Tunneling and Underground Space Technology, 26(5) (2011) 595–603.

[17]   E. Farrokh, J. Rostami, C. Laughton, Study of various models for estimation of advance rate of hard rock TBMs, Tunneling and Underground. Space Technology, 30 (2012) 110–123.

[18]   A. Palmström, RMi- a rock mass characterization system for rock engineering purposes Oslo University, 1995.

[19]   N. Barton, TBM performance estimation in rock using QTBM, Tunnels and Tunneling International, (1999).

[20]   Z.T. Bieniawski, Rock Mass Excavability (RME) index, in:  ITA World Tunnelling Congress, Seoul, 2006.

[21]   E. Avunduk, H. Copur, Empirical modeling for predicting excavation performance of EPB TBM based on soil properties. Tunneling and Underground Space Technology, 71 (2018) 340–353.

[22]   U. Ates, N. Bilgin, H. Copur, Estimating torque, thrust and other design parameters of different type TBMs with some criticism to TBMs used in Turkish tunneling projects, Tunneling and Underground Space Technology, 20 (2014) 46–63.

[23]   J.P. Vargas, J.C. Koppe, C. Pérez, Monte Carlo simulation as a tool for tunneling planning. Tunneling and Underground Space Technology, 40 (2014) 203–209.

[24]   A. Babaei, Application of Monte Carlo simulation in tunneling, Iran University of Science and Technology, 2003 (in Persian).

[25]   Shabani, O. Sayyadi, K. Goshtasbi, A. Roodbari, Investigation of Uncertainties in tunneling projects costs using Monte Carlo- Case study of Dasht Zahab Tunnel. , in:  Seventh Iranian Tunnelling Conference, Tehran, 2006 (in Persian).

[26]   G. Piaggio, J.P. Novel, G.W. Bianchi, A. Bochon, Probabilistic estimation of project duration using TBM prediction models: application to the safety gallery of the Fréjus Tunnel, in:  World Tunneling Congress (2013) 1141–1148., 2013.

[27]   Frenzel, Modeling uncertainty in cutter wear prediction for tunnel boring machines, in:  GeoCongress, 2012, pp. 3199–3208.

[28]   S. Eftekhari, S.M. Mokhtarian, A. Baghbanan, Advance rate prediction of tunnel boring machine using artificial neural networks and Monte Carlo methods, Journal of geotechnical geology 10(4) (2014) 255-264 (in Persian).

[29]   H. Copur, H. Aydin, N. Bilgin, C. Balci, D. Tumac, C. Dayanc, Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model, Tunneling and Underground Space Technology 42 (2014) 1–14.

[30]   S. Yagiz, H. Karahan, Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass, International Journal of Rock Mechanics and Mining Sciences 80 (2017) 308–315.

[31]   A. Salimi, R.S. Faradonbeh, M. Monjezi, C. Moormann, TBM performance estimation using a classification and regression tree (CART) technique, Bulletin of Engineering Geology and the Environment 77 (2018) 429-440.

[32]   M.R. Maleki, Rock Joint Rate (RJR); a new method for performance prediction of tunnel boring machines (TBMs) in hard rocks, Tunneling and Underground Space Technology 73 (2018) 261–286.

[33]   V.B. Maji, G.V. Theja, A New Performance Prediction Model for Rock TBMs, Indian Geotechnical Journal 47(3) (2017) 364–372.

[34]   A. Johari, F. Sani, M. Parvaz, Reliability analysis of infinite soil slope stability using distribution of variables composition method, in:  6th national civil engineering congress, Semnan, 2011 (in Persian).

[35]   N.R. Morgenstern, Managing risk in geotechnical engineering, in:  10th Pan American Conference on Soil Mechanics and Foundation Engineering, 1995.

[36]   M. Cai, Rock mass characterization and rock property variability considerations for tunnel and cavern design. Rock Mechanics and Rock Engineering, 44(4) (2011) 379–399.

[37]   M.M. Aral, M.L. Maslia, Application of Monte Carlo Simulation to Analytical Contaminant Transport Modeling, in:  Groundwater Quality Modeling and Management Under Uncertainty, Environmental and Water Resources, Institute of the American Society of Civil Engineers, Philadelphia, PA, American Society of Civil Engineers, 2003, pp. 305–312.

[38]   M. Ghias, An introduction to Monte Carlo simulation method, Basparesh Quarterly, 4(1) (2014) 67-77 (in Persian).

[39]   BIPM, IFCC, ISO, Evaluation of measurement data Supplement 1 to the ‘Guide to the expression of uncertainty in measurement’ Propagation of distributions using a Monte Carlo method, Joint committee for guides in metrology, 101 (2008). JCGM, 2008.

[40]   Concept of uncertainty and sensitivity analysis [Online], in: https:// https://rdreview.jaea.go.jp/review_en/2010/6_6f6_13.html

[41]   F. Macias, Hard rock tunnel boring: performance predictions and cutter life assessments, Norwegian University of Science and Technology, 2016.

[42]   L. Ozdemir, R. Miller, F.D. Wang, Mechanical tunnel boring prediction and machine design, Final project to NSF APR73-07776-A03, Colorado School of Mines, Golden, Colorado, 1978.

[43]   J. Rostami, L. Ozdemir, A New Model for Performance Prediction of Hard Rock TBMs, in:  Rapid Excavation and Tunneling Conference, 1993, pp. 793-809.

[44]   J. Rostami, Development of a force estimation model for rock fragmentation with disc cutters through theoretical modelling and physical measurement of crushed zone pressure, Colorado School of Mines, 1997.

[45]   S. Yagiz, J. Rostami, L. Ozdemir, Colorado School of Mines approach for predicting TBM performance, in:  ISRM International Symposium - EUROCK 2012, International Society for Rock Mechanics and Rock Engineering, Stockholm, Sweden, 2012.

[46]   S.N. Cheema, Development of a Rock Mass Boreability Index for the Performance of Tunnel Boring Machines, Colorado school of Mines, 1999.

[47]   J. Hassanpour, J. Rostami, S.T. Azali, J. Zhao, Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks: a case history of Karaj water conveyance tunnel, Iran, Tunneling and Underground Space Technology 43 (2014) 222–231.

[48]   J. Hassanpour, J. Rostami, M. Khamehchiyan, A. Bruland, H.R. Tavakoli, TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel, Rock Mechanics and Rock Engineering 43(4) (2010) 427–445.