Prediction model of tunnel boring machine performance by ensemble neural networks

References

• Alber , M. 1996 . “ Prediction of penetration and utilization for hard rock TBMs ” . In Eurock'96 , Edited by: Barla , G. 721 – 725 . Rotterdam : Balkema .
   Google Scholar
• Alber , M. 2000 . Advance rates of hard rock tbms and their effects on project economics . Tunnelling Underground Space Technol. , 15 ( 1 ) : 55 – 64 .
   Web of Science ®Google Scholar
• Barton , N. 2000 . TBM Tunnelling in Jointed and Faulted Rock , Rotterdam : Balkema .
   Google Scholar
• Bruines , P. A. 2001 . “ The use of neurofuzzy modeling for performance prediction of tunnel boring machines ” . In Modern Tunnelling Science and Technology , Edited by: Kimura , M. , Adachi , T. and Tateyama , K. 583 – 588 . Lisse, , The Netherlands : Swets & Zeitlinger .
   Google Scholar
• Bruland , A. . Hard rock tunnel boring . Doctoral thesis, Norwegian University of Science and Technology . Trondheim.
   Google Scholar
• Efron , B. and Tibshirani , R. 1993 . An Introduction to the Bootstrap , New York : Chapman & Hall .
   Google Scholar
• Farmer , I. W. and Glossop , N. H. 1980 . Mechanics of disc cutter penetration . Tunnels Tunnelling Int. , 12 ( 6 ) : 22 – 25 .
   Google Scholar
• Gong , Q. M. . Development of a rock mass characteristics model for TBM penetration rate prediction . PhD thesis, Nanyang Technological University . Singapore.
   Google Scholar
• Gong , Q. M. , Zhao , J. and Jiang , Y. S. 2007 . In situTBM penetration tests and rock mass boreability analysis in hard rock tunnels . Tunnelling Underground Space Technol. , 22 ( 3 ) : 303 – 316 .
   Web of Science ®Google Scholar
• Graham , P. C. 1976 . “ Rock exploration for machine manufacturers ” . In Exploration for Rock Engineering , Edited by: Bieniawski , Z. T. Vol. 1 , 173 – 180 . Rotterdam : Balkema .
   Google Scholar
• Grandori , R. , Sem , M. , Lembo-Fazio , A. and Ribacchi , R. . Tunnelling by double shield TBM in the Hong Kong granite . 8th International Congress for Rock Mechanics . Vol. 1 , pp. 569 – 574 .
   Google Scholar
• Grima , M. A. , Bruines , P. A. and Verhoef , P. N. W. 2000 . Modeling tunnel boring machine performance by neuro-fuzzy methods . Tunnelling Underground Space Technol. , 15 ( 3 ) : 259 – 269 .
   Web of Science ®Google Scholar
• Hamilton , W. H. and Dollinger , G. L. 1979 . “ Optimizing tunnel boring machine and cutter design for greater boreability ” . In RETC Proceedings Vol. 1 , 280 – 296 .
   Google Scholar
• Hughes , H. M. 1986 . The relative cuttability of coal measures rock . Min. Sci. Technol. , 3 : 95 – 109 .
   Google Scholar
• Innaurato , N. , Mancini , R. , Rondena , E. and Zaninetti , A. 1991 . “ Forecasting and effective TBM performance in a rapid excavation of a tunnel in Italy ” . In Proceedings of the 7th International Congress on Rock Mechanics , Edited by: Wittke , W. 1009 – 1014 . Rotterdam : Balkema .
   Google Scholar
• Jakubek , S. M. and Strasser , T. I. 2004 . Artificial neural networks for fault detection in large-scale data acquisition systems . Eng. Appl. Artif. Intell. , 17 : 233 – 248 .
   Google Scholar
• Jiang , N. , Zhao , Z. Y. and Ren , L. Q. 2003 . Design of structural modular neural networks with genetic algorithm . Adv. Eng. Software , 34 : 17 – 24 .
   Google Scholar
• Laughton , C. and Nelson , P. P. 1996 . “ The development of rock mass parameters for use in the prediction of tunnel boring machine performance ” . In Eurock'96 , Edited by: Barla , G. 727 – 733 . Rotterdam : Balkema .
   Google Scholar
• McFeat-Smith , I. 1999 . Mechanised tunnelling for Asia , Workshop Manual, IMS Tunnel Consultancy Ltd .
   Google Scholar
• McFeat-Smith , I. and Askilsrud , O. G. . Tunnel boring machines in Hong Kong . RECT Proceedings . pp. 401 – 413 .
   Google Scholar
• Morimoto , T. and Hori , M. 1986 . Performance Characteristics of a Tunnel Boring Machine from the Geomechanical Viewpoint . International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts , 23 ( 1 ) : 55 – 66 .
   Web of Science ®Google Scholar
• Nelson , P. P. . Tunnel boring machine performance in sedimentary rock . Doctoral dissertation, the Graduate School of Cornelll University . USA. pp. 438
   Google Scholar
• Nelson , P. P. , Yousof , A. , Al-Jalil and Laughton , C. . Improved strategies for TBM performance prediction and project management . RETC Proceedings . pp. 963 – 979 .
   Google Scholar
• O'Rourke , J. E. , Spring , J. E. and Coudray , S. V. 1994 . “ Geotechnical parameters and tunnel boring machine performance at Goodwill Tunnel, California ” . In Rock Mechanics Models and Measurements: Challenges from Industry , Edited by: Nelson , P. and Laubach , S. E. Rotterdam : Balkema .
   Google Scholar
• 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. , 38 ( 2 ) : 105 – 127 .
   Web of Science ®Google Scholar
• Rostami , J. . Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure . Doctoral dissertation, Department of Mining Engineering, Colorado School of Mines . Golden, CO. pp. 382
   Google Scholar
• Rumelhart , D. E. , Hinton , G. E. and Williams , R. J. 1986 . “ Learning internal representations by error propagation ” . In Parallel Distributed Processing: Foundation , Edited by: Rumelhart , D. E. and McClelland , J. L. Vol. 1 , 318 – 362 . Cambridge, MA : MIT Press .
   Google Scholar
• Sapigni , M. , Berti , M. , Bethaz , E. , Busillo , A. and Cardone , G. 2002 . TBM performance estimation using rock mass classifications . Int. J. Rock Mech. Min. Sci. , 39 : 771 – 788 .
   Web of Science ®Google Scholar
• Sharkey , A. , ed. 1999 . Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , London : Springer .
   Google Scholar
• Sherrod , H. P. 1996 . “ Nonlinear regression analysis program ” . In NLERG Manual 70
   Google Scholar
• Sollich , P. and Krogh , A. 1996 . “ Learning with ensembles: how over-fitting can be useful ” . In Advances in Neural Information Processing Systems 8 , Edited by: Touretzky , D. S. , Mozer , M. C. and Hasselmo , M. E. 190 – 196 . Cambridge, MA : MIT Press .
   Google Scholar
• Sundin , N. O. and Wanstedt , S. 1994 . Rock Mechanics Models and Measurements: Challenges from Industry , Edited by: Nelson , P. and Laubach , S. E. 8 Rotterdam : Balkema .
   Google Scholar
• Wanner , H. and Aeberli , U. . Tunnelling machine performance in jointed rock . 4th Congress of the International Society for Rock Mechanics . Vol. 1 , pp. 573 – 580 .
   Google Scholar
• Zhao , J. , Gong , Q. M. and Eisensten , Z. 2007 . Tunnelling through a frequently changing and mixed ground: a case history in Singapore . Tunnelling Underground Space Technol. , 22 ( 4 ) : 388 – 400 .
   Web of Science ®Google Scholar
• Zhao , Z. Y. 2006 . Steel column under fire: a neural network based strength model . Adv. Eng. Software , 37 ( 2 ) : 97 – 105 .
   Web of Science ®Google Scholar