Probabilistic Analysis of TBM Advance Rate Prediction Models

Document Type : Research Article

Authors

1 Shahrood University of Technology

2 Department of Petrol um, Mining and Geo physic, Shahrood University of Technology

3 Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

The overall purpose of this study is to use probabilistic methods for the estimation of the advanced rate of full-face tunnel boring machines. To collect appropriate input parameters, Monte Carlo Simulation was utilized. Then, the calculation phase was conducted applying established models on input data and probability density functions of output data were obtained. The results show that the average advance rates calculated by QTBM and CSM models were closer to the average value of the actual advance rates. In addition, using probabilistic methods in combination with TBM prediction models helps to estimate the range of advance rates more confidently.
 

Keywords

Main Subjects


[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.