Utilising of linear and non-linear prediction tools for evaluation of penetration rate of Tunnel Boring Machine in hard rock condition

International Journal of Mining and Mineral Engineering

Volumn 4, Issue 3, 2013, Pages 249-264

  Salimi, Alireaza ^a   Esmaeili, Mohammad ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

ANN; Artificial neural network; Multiple regression analyses; Penetration rate; TBM; Tunnel boring machine

Indexed keywords

BORING MACHINES (MACHINE TOOLS); BORING TOOLS; COMPRESSIVE STRENGTH; FORECASTING; NEURAL NETWORKS; REGRESSION ANALYSIS; ROCK MECHANICS; ROCKS; TENSILE STRENGTH; TUNNELING MACHINES;

BRAZILIAN TENSILE STRENGTHS; LINEAR MULTIPLE REGRESSION MODEL; MULTIPLE REGRESSION ANALYSIS; PENETRATION RATES; PREDICTION PERFORMANCE; TUNNEL BORING MACHINE(TBM); TUNNEL BORING MACHINES; UNIAXIAL COMPRESSIVE STRENGTH;

CONSTRUCTION EQUIPMENT;

EID: 84878144057     PISSN: 1754890X     EISSN: 17548918     Source Type: Journal    
DOI: 10.1504/IJMME.2013.053172     Document Type: Article

References (67)

1. Alsmadi, M.K.S., Omar, K.B. and Noah, S.A. (2009) 'Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm', Int. J. Comput. Sci. Netw. Secur., Vol. 9, No. 4, pp.378-383.
2. Alvarez Grima, M. and Babuska, R. (1999) 'Fuzzy model for the prediction of unconfined compressive strength of rock samples', International Journal of Rock Mechanics and Mining Sciences, Vol. 36, No. 3, pp.339-349. (Pubitemid 29338170)
3. Alvarez Grima, M., Brunies, P.A. and Verhoef, P.N.W. (2000) 'Modeling tunnel boring machine by neuro-fuzzy methods', Tunneling and Underground Space Technology, Vol. 15, No. 3, pp.259-269.
4. Anderson, J.A. (1995) An Introduction to Neural Networks, MIT Press, Cambridge.
5. TBM', Tunnels Tunnelling Int., Vol. 31, No. 9, pp.41-48.
6. Barton, N. (2000) TBM Tunneling in Jointed and Faulted Rock, Balkema, Rotterdam.
7. Bejari, H., Kakaie, R., Ataei, M. and Khademi Hamidi, J. (2011) 'Simultaneous effects of joint spacing and joint orientation on the penetration rate of a single disc cutter', Mining Sci. Technol. (China), Vol. 21, No. 4, pp.507-512.
8. Benardos, A.G. and Kaliampakos, D.C. (2004) 'Modeling TBM performance with artificial neural networks', Tunneling and Underground Space Tech., Vol. 19, No. 6, pp.597-605. (Pubitemid 39239107)
9. Bieniawski, Z.T. (1973) 'Engineering classification of jointed rock masses', Trans. S. Afr. Inst. Civ. Eng., Vol. 15, pp.335-344.
10. Brown, E. (1981) Rock Characterization Testing and Monitoring, ISRM Suggested Methods.
11. Bruland, A. (1998) Hard Rock Tunnel Boring, Doctoral Thesis, Norwegian University of Science and Technology, Trondheim.
12. Bruland, A. (1999) Hard Rock Tunnel Boring: Advance Rate and Cutter Wear, Norwegian Institute of Technology (NTNU), Trondheim, Norway.
13. Cassinelli, F., Cina, S., Innaurato, N., Mancini, R. and Sampaolo, A. (1982) 'Power consumption and metal wear in tunnel-boring machines: analysis of tunnel boring machine operation in hard rock', Tunneling, Vol. 82, pp.73-81. (Pubitemid 13501354)
14. Chauvin, Y. and Rumelhart, D.E. (1995) Backpropagation: Theory, Architectures and Applications, Erlbaum, Mahwah, NJ.
15. Cigla, M., Yagiz, S. and Ozdemir, L. (2001) 'Application of tunnel boring machines in underground mining development', 17th Int. Mining Congress & Exhibition of Turkey, pp.155-164.
16. Dollinger, G.L. and Handewith, H.J. (1998) 'Use of the punch test forestimating TBM performance', Tunnel. Underground Space Technol., Vol. 13, No. 4, pp.403-408. (Pubitemid 29176339)
17. Farmer, I.W. and Glossop, N.H. (1980) 'Mechanics of disc cutter penetration', Tunnels Tunnell. Int., Vol. 12, No. 6, pp.22-25.
18. Finol, J., Guo, Y.K. and Jing, X.D. (2001) 'A rule based fuzzy model for the prediction of petrophysical rock parameters', Journal of Petroleum Science and Engineering, Vol. 29, No. 2, pp.97-113. (Pubitemid 32434781)
19. Gokceoglu, C. (2002) 'A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition', Engineering Geology, Vol. 66, No. 1, pp.39-51.
20. Gokceoglu, C. and Zorlu, K. (2004) 'A fuzzy model to predict the uniaxial compressive strength and modulus of elasticity of a problematic rock', Engineering Applications of Artificial Intelligence, Vol. 17, No. 1, pp.61-72.
21. Gokceoglu, C., Sonmez, H. and Zorlu, K. (2009a) 'Estimating the uniaxial compressive strength of some clay bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models', Expert Systems, Vol. 26, No. 2, pp.176-190.
22. Gokceoglu, C., Zorlu, K., Ceryan, S. and Nefeslioglu, H.A. (2009b) 'A comparative study on indirect determination of degree of weathering of granites from some physical and strength parameters by two soft computing techniques', Materials Characterization, Vol. 60, No. 11, pp.1317-1327.
23. Gong, Q.M. and Zhao, J. (2007) 'Influence of rock brittleness on TBM penetration rate in Singapore granite', Tunnelling and Underground Space Technol., Vol. 22, No. 3, pp.317-324. (Pubitemid 46223951)
24. Gong, Q.M. and Zhao, J. (2009) 'Development of a rock mass characteristics model for TBM penetration rate prediction', Int. J. Rock Mech. Min. Sci., Vol. 46, No. 1, pp.8-18.
25. Gong, Q.M., Zhao, J. and Jiao, Y.Y. (2005) 'Numerical modeling of the effects of joint orientation on rock fragmentation by TBM cutters', Tunnell. Undergr. Space Technol., Vol. 20, No. 2, pp.183-191. (Pubitemid 39454776)
26. Graham, P.C. (1976) 'Rock exploration for machine manufacturers', in Bieniawski, Z.T. (Ed.): Exploration for Rock Engineering, Balkema, Johannesburg, pp.173-180.
27. Handewith, H.J. (1970) 'Predicting the economic success of continuous tunnelingand hard rock', 71st Annual General Meeting of the CIM, Vol. 63, pp.595-599.
28. Hughes, H.M. (1986) 'The relative cuttability of coal measures rock', Min. Sci. Technol., Vol. 3, pp.95-109. (Pubitemid 16490244)
29. Kahraman, S., Altun, H., Tezekici, B.S. and Fener, M. (2006) 'Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks', International Journal of Rock Mechanics and Mining Sciences, Vol. 43, pp.157-164. (Pubitemid 41671409)
30. Khandelwal, M. and Singh, T.N. (2006) 'Prediction of blast induced ground vibrations and frequency in opencast mine - a neural network approach', J. Sound Vib., Vol. 289, pp.711-725.
31. Lee, I.M. and Lee, J.H. (1996) 'Predicting of pile bearing capacity using artificial neural networks', Computer and Geotechnics, Vol. 18, No. 3, pp.189-200.
32. Lislerud, A. (1988) 'Hard rock tunnel boring: prognosis and costs', Tunneling and Underground Space Technology, Vol. 3, No. 1, pp.9-17. (Pubitemid 18618941)
33. McFeat-Smith, I. (1977) 'Rock property testing for the assessment of tunneling machine performance', Tunnels and Tunneling, pp.29-33.
34. Monjezi, M., Rezaei, M. and Yazdian, A. (2009) 'Prediction of backbreak in open-pit blasting using fuzzy set theory', J. Expert Syst. Appl., Vol. 37, pp.2637-2643.
35. Neaupanea, K.M. and Achet, S.H. (2004) 'Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya', J. Eng. Geol., Vol. 74, pp.213-226. (Pubitemid 38830892)
36. Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. (2003) 'A Mamdani model to predict the weighted joint density', Lecture Notes in Artificial Intelligence, Vol. 2773, pp.1052-1057. (Pubitemid 37230785)
37. Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. (2006) 'Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles', Engineering Geology, Vol. 85, pp.251-269. (Pubitemid 43827502)
38. Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. (2008) 'An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps', Engineering Geology, Vol. 97, Nos. 3-4, pp.171-191.
39. Nelson, P.P. and O'Rourke, T.D. (1983) Tunnel Boring Machine Performance in Sedimentary Rocks, Report to Goldberg-Zoino Associates of New York, PC, by School of Civil and Environmental of Civil Engineering, Cornell University, Ithaca, NY.
40. Nielsen, R.H. (1998) 'Neurocomputing', Picking the Human Brain, IEEE, Vol. 3, pp.36-41.
41. Okubo, S., Kfukie, K. and Chen, W. (2003) 'Expert systems for applicability of tunnel boring machine in Japan', Rock Mechanics and Rock Engineering, Vol. 36, No. 4, pp.305-322. (Pubitemid 41523037)
42. Osanloo, M. (1998) Drilling methods, Tehran, Sadra.
43. Ozdemir, L. (1977) Development of Theoretical Equations for Predicting Tunnel Borability, PhD Thesis, T-1969, Colorado School of Mines, Golden, CO, USA.
44. Ozdemir, L. (2002) Personnel Communication, Earth Mechanics Institute of Colorado School of Mines, Golden, CO, USA.
45. Ribacchi, R. and Lembo-Fazio, A. (2005) 'Influence of rock mass parameters on the performance of a TBM in a Gneissic formation (Varzo tunnel)', Rock Mech. Rock Eng., Vol. 38, No. 2, pp.105-127.
46. Rostami, J. and Ozdemir, L. (1993) 'A new model for performance prediction of hard rock TBM', Proceedings of the Rapid Excavation and Tunnelling Conference, Society for Mining and Metallurgy and Exploration Inc., Boston, pp.793-809. (Pubitemid 23697865)
47. Sapigni, M., Berti, M., Behtaz, E., Busillo, A. and Cardone, G. (2002) 'TBM performance estimation using rock mass classification', Int. J. Rock Mech. Min. Sci. Geomech., Vol. 39, pp.771-788.
48. Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A. and Kayabasi, A. (2006) 'Estimation of rock modulus: for intact rock with an artificial neural network and for rock masses with a new empirical equation', International Journal of Rock Mechanics and Mining Sciences, Vol. 43, pp.224-235. (Pubitemid 43055524)
49. Szwedzicki, T. (1998) 'Draft ISRM suggested method for determining theindentation hardness index of rock materials', Int. J. Rock Mech. Min. Sci., Vol. 35, No. 6, pp.831-835.
50. Tarkoy, P.J. (1975) Rock Hardness Index Properties and Geotechnical Parameters for Predicting Tunnel Boring Machine Performance, PhD Thesis, University of Illinois at Urbana-Champaign.
51. Tiryaki, B. (2008) 'Application of artificial neural networks for predicting the cuttability of rocks by drag tools', Tunneling and Underground Space Technology, Vol. 23, No. 3, pp.273-280. (Pubitemid 351309118)
52. Wasserman, P.D. (1989) Neural Computing Theory and Practice, Van Nostrand Reinhold, New York.
53. Yagiz, S. (2002) Development of Rock Fracture and Brittleness Indices to Quantify the Effects of Rock Mass Features and Toughness in the CSM Model Basic Penetration for Hard Rock Tunneling Machines, PhD Thesis, Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA.
54. Yagiz, S. (2006) 'TBM performance prediction based on rock properties', Proceedings of Multiphysics Coupling and Long Term Behavior in Rock Mechanics, EUROCK'06, pp.663-670.
55. Yagiz, S. (2008) 'Utilizing rock mass properties for predicting TBM performance in hard rock condition', Tunneling and Underground Space Technology, Vol. 23, pp.326-339. (Pubitemid 351309114)
56. Yagiz, S., Gokceoglu, C., Sezer, E. and Iplikci, S. (2009) 'Application of two non-linear prediction tools to the estimation of tunnel boring machine performance', Engineering Applications of Artificial Intelligence, Vol. 22, pp.808-814.
57. Yagiz, S., Rostami, J. and Ozdemir, L. (2008) 'Recommended rock testing methods for predicting TBM performance: focus on the CSM and NTNU Models', Proceedings of the ISRM International Symposium, 5th ASRM, Tehran, Iran, pp.1523-1530.
58. Yilmaz, I. and Yuksek, A.G. (2008) 'An example of artificial neural network application for indirect estimation of rock parameters', Rock Mechanics and Rock Engineering, Vol. 41, No. 5, pp.781-795.
59. Yilmaz, I. and Yuksek, A.G. (2009) 'Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison', International Journal of Rock Mechanics and Mining Sciences, Vol. 46, No. 4, pp.803-810.
60. Yoo, C. and Kim, J. (2007) 'Tunneling performance prediction using an integrated GIS and neural network', Computer and Geotechnics, Vol. 34, pp.19-30. (Pubitemid 44914551)
61. Aeberli, U. and Wanner, W.J. (1978) 'On the influence of discontinuities at the application of tunneling machines', Proceedings of the3rd International Congress IAEG, Madrid, pp.7-14.
62. Buchi, E. (2007) The Influence of Joints on TBM Performance, Private Communication.
63. Chandok, J.S., Kar, I.N. and Tuli, S. (2008) 'Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and backpropagation neural networks', J. Rock Energy Convers. Manag., Vol. 49, pp.1989-1998.
64. Cheema, S (1999) Development of a Rock Mass Boreability Index for the Performance of Tunnel Boring Machines, PhD Dissertation, Colorado School of Mines, Golden, CO.
65. Howarth, D.F. (1981) 'The effect of jointed and fissured rock on the performance of tunnel boring machines', Proceedings of the International Symposium on Weak Rock, Tokyo, pp.1069-1074.
66. O'Rourke, J.E., Spring, J.E. and Coudray, S.V. (1994) 'Geotechnical parameters and tunnel boring machine performance at Good will Tunnel, California', in Nelson, P.P. and Laubach, S.E. (Eds.): Proceedings of the 1st North American Rock Mechanics Symposium, Rotterdam, Balkema, Austin, Texas.
67. Thuro, K. and Plinninger, R.J. (2003) 'Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability', Proceedings of the 10th International Congress ISRM, South African Institute on Mineral Metallurgy, pp.1-7.