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MECHANISED TUNNELLING | TECHNICAL


REFERENCES ● Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N., &


Yagiz, S. (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. TUST 63:29–43


● Blindheim, O.T. (1979) Boreability predictions for tunnelling. Ph.D. thesis. NTNU


● Bruland, A. (1998) Hard rock tunnel boring: vol 1–10, Ph.D. NTNU ● Company SCE (2004) Geological and Engineering Geological Report for Ghomrood Water Conveyance Tunnel Project (Lots 3 & 4) (Unpublished report)


● Deere, D.W., Keis, S., & Watts, C. (2004) The Manapouri Tailrace Tunnel No. 2 construction. North American Tunnelling, pp 421–432, Ozdemir, L. (ed)


● Delisio, A., & Zhao, J. (2014) A new model for TBM performance prediction in blocky rock conditions. TUST 43:440–452


● Farrokh, E., Rostami, J., & Laughton, C. (2012) Study of various models for estimation of penetration rate of hard rock TBMs. TUST 30:110– 123. https://doi.org/10.1016/j.tust.2012.02.012


● Fatemi, S.A., Ahmadi, M., & Rostami, J. (2016) Evaluation of TBM performance prediction models and sensitivity analysis of input parameters. Bull Eng Geol Environ. https://doi.org/10.1007/ s10064- 016-0967-2


● Gong, Q.M., & Zhao, J. (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46(1):8–18


● Goodarzi, S., Hassanpour, J., Yagiz, S., & Rostami, J. (2021) Predicting TBM performance in soft sedimentary rocks: Zagros Mountains Water Tunnel Projects. TUST. https://doi.org/10.1016/j.tust.2020.103705


● Hassanpour, J., Rostami, J., Khamehchiyan, M., & Bruland, A. (2009) Developing new equations for TBM performance prediction in carbonate argillaceous rocks: case history of Nowsood water conveyance tunnel. Int J Geomech Geoeng 4:287–297


● Hassanpour, J., Rostami, J., Khamehchiyan, M., Bruland, A., & Tavakoli, H. R. (2010) TBM performance analysis in pyroclastic rocks, a case history of Karaj Water Conveyance Tunnel. J Rock Mech Rock Eng 4:427–445


● Hassanpour, J., Rostami, J., & Zhao, J. (2011) A new hard rock TBM performance prediction model for project planning. TUST 26:595–603


● Hassanpour, J., Ghaedi Vanani, A. A., Rostami, J., & Cheshomi, A. (2016) Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2). TUST 52:147–1456


● Hassanpour, J., Firouzei, Y., & Hajipour, G. (2021) Actual performance analysis of a double shield TBM through sedimentary and low to medium grade metamorphic rocks of Ghomrood water conveyance tunnel project (lots 3 and 4). Bull Eng Geol Env. https://doi.org/10.1007/ s10064-020-01947-z


● Imensazan consulting engineers (ICE) (2009) Golab water transfer tunnel engineering geology studies. (Unpublished)


● Jain, P. (2014) Evaluation of engineering geological and geotechnical properties for the performance of a tunnel boring machine in Deccan traps rocks: case study from Mumbai, India. Ph.D. Indian Institute of Technology (Unpublished)


● Jain, P., Naithani, A.K., & Singh, T.N. (2014) Performance characteristics of tunnel boring machine in basalt and pyroclastic rocks of Deccan traps: a case study. Int J Rock Mech Geotech Eng 6:36–47


● Khademi Hamidi, J., Shahriar, K., Rezai, B., & Rostami, J. (2010) Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. TUST. https://doi.org/10.1016/j.tust.2010.01.008


● Koopialipoor, M., Tootoonchi, H., Armaghani, J., Mohamad, E.T., & Hedayat, A. (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Environ 78:6347–6360. https://doi.org/10.1007/s10064-019-01538-7


● Mahdevari, S., Shahriar, K., Yagiz, S., & Akbarpour Shirazi, M. (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229


● Marcher, T., Erharter, G., & Winkler, M. (2020) Machine Learning in tunnelling – capabilities and challenges. Int J Geomech Tunnell. https:// doi.org/10.1002/geot.202000001


● Morgenroth, J., Khan, U.T., & Perras, M.A. (2019) An overview of opportunities for machine learning methods in underground rock engineering design. MDPI Geosci 9(12):504. https://doi.org/10.3390/ geosc iences9120504


● Pourhashemi, S.M., Ahangari, K., Hassanpour, J., & Eftrekhari, S.M. (2021) TBM performance analysis in very strong and massive rocks: Case study of Kerman water conveyance tunnel, Iran. Int J Geomech Geoeng. https://doi.org/10.1080/17486025.2021.19124 10


● Rostami, J. (2013) Study of pressure distribution within the crushed zone in the contact area between rock and disc cutters. Int J Rock Mech Min Sci 57:172–186


● Salimi, A. (2021) Investigation and Evaluation of Rock Mass Characteristics for Development of New TBM Performance Prediction Model in Hard Rock Conditions. Ph.D. Institute of Geotechnical Engineering (IGS), University of Stuttgart (Published)


● Salimi, A., Rostami, J., Moormann, C., & Delisio, A. (2016) Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. TUST 58:236–246


● Salimi, A., Rostami, J., & Moormann, C. (2019a) Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms. TUST. https://doi.org/10.1016/j.tust. 2019.103046


● Salimi, A., Rostami, J., & Moormann, C. (2019b) Development of a New Models for Prediction of TBM Performance in Hard Rock Condition Based on Rock Type. TBM-DiGs 2019: Tunnel Boring Machines in Difficult Grounds, 4th international conference. Colorado School of Mines


● Therneau, T.M., Atkinson, E. J. (1997) An introduction to recursive partitioning using the RPART routines. Technical report 61. Rochester: Section of Biostatistics, Mayo Clinic


● URS Company (2003) Manapouri Power Station Second Tailrace - Tunnel Engineering Geological Construction Report, Prepared for Meridian Energy Ltd. (Unpublished report)


● Wu, X., Kumar, V., Quinlan, R. J., Ghosh, J., Yang, Q., & Motoda, H. et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37


● 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 tunnelling machines. Ph.D. Colorado School of Mines (CSM)


● Yagiz, S., Gokceoglu, C., Sezer, E., & Iplikci, S. (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808–814


● Zhang, Q., Hu, W., Liu, Z., & Tan, J. (2020) TBM performance prediction with Bayesian optimization and automated machine learning. TUST. https://doi.org/10.1016/j.tust.2020.103493


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