شرکت سابیر بین الملل.، 1395. گزارش مطالعات زمین شناسی و زمین شناسی مهندسی توسعه جنوبی خط 6 متروی تهران.
صمدی، ح.، حسن پور، ج.، 1398. ارزیابی صحت روشهای تعیین پارامترهای اپراتوری ماشین EPB (مطالعه موردی خط 7 متروی تهران- قطعه شرقی-غربی). سیزدهمین کنفرانس انجمن تونل ایران، 21-22 آبان، تهران.
Afradi, A., Ebrahimabadi, A., Hallajian, T., 2016. Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM), case study: beheshtabad water conveyance tunnel in Iran. Asian Journal of Water, Environment and Pollution, 16(1): 49–57.
Ates, U., Bilgin, N., Copur, H., 2014. 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, 40: 46–63.
Benardos, AG., Kaliampakos, DC., 2004. Modeling TBM performance with artificial neural networks. Tunneling and Underground Space Technology, 19(3): 597–605.
Bilgin, N., Copur, H., Balci, C., Tumac, D., Akgul, M., Yuksel, A., 2008. The selection of a TBM using full scale laboratory tests and comparison of measured and predicted performance values in Istanbul Kozyatagi-Kadikoy metro tunnels. Proceeding of the 34th Annual Meeting of the International Tunneling and Underground Space Association, 19–25 September, 1509–1517.
Cigla, M., Yagiz, S., Ozdemir, L., 2001. Application of tunnel boring machines in underground mine development. Proceeding of the 17th International Mining Congress and Exhibition of Turkey, 19-22 June, Ankara, 155–164.
Delisio, A., Zhao, J., Einstein, H., 2013. Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel. Tunnelling and Underground Space Technology, 33: 131–142.
Firouzei, Y., Hassanpour, J., Pourhashemi, S. M., 2019. Tunneling with a soft rock EPB machine in hard rock condiyions, the experience of Tehran metro line 6 southern expansion sector. Proceeding of the 4th International Conference of TBMDiGs, 13-15 November, Colorado State, USA, 110–119.
Gao, X., Shi, M., Song, X., Zhang, Ch., Zhang, H., 2019. Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 98: 225–235.
Gholamnejad, J., Tayarani, N., 2010. Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Mining Science Technology (China), 20(5): 727–733.
Godinez, R., Yu, H., Mooney, M., Gharahbagh, E., Frank, G., 2015. Earth pressure balance machine cutterhead torque modeling: Learning from machine data. Proceeding of the Rapid Excavation and Tunneling Conference, 7–10 June, USA.
Gong, Q., Zhao, J., 2009. Development of a rock mass characteristics model for TBM penetration rate prediction. International Journal of Rock Mechanic and Mining Sciences, 46(1): 8–18.
Grima, M. A., Bruines, P. A., Verhoef, P. N. W., 2000. Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling and Underground Space Technology, 15(3): 259–269.
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 (KWCT). Journal of Rock mechanics and Rock Engineering, 4: 427–445.
Hassanpour, J., Rostami, J., Zhao, J., 2011. A new hard rock TBM performance prediction model for project planning. Tunneling and Underground Space Technology, 26: 595–603.
Huang, L., Li, J., Hao, H., Li, X., 2018. Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning. Tunnelling and Underground Space Technology, 81: 265–276.
Jalalkamali, A., Moradi, M., Moradi, N., 2015. Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. International Journal of Environmental Science and Technology, 12(4): 1201–1210.
JSCE (Japan Society of Civil Engineers). 2007. Standard Specifications For Tunneling–Shield Tunnels.
Liu, B., Wang, R., Zhao, G., Guo, X., Wang, Y., Lic, J., Wang, S., 2020. Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm. Tunnelling and Underground Space Technology, 95.
Menhrotra, K., Mohan, C. K., Ranka, S., 1997. Elements of Artificial Neural Networks. Cambridge: MIT Press.
Natarajan, B. K., 1995. Sparse approximate solutions to linear systems. SIAM Journal on Computing, 24(2): 227–234.
Neter, J., 1999. Applied linear regression models. 3rd (Eds.). The McGraw-Hill companies. ISBN: 0-256-08601-x.
Salimi, A., Rostamib, J., Moormanna, Ch., 2019. Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms. Tunnelling and Underground Space Technology, 92.
Shi, H., Yang, H., Gong, G., Wang, L., 2011. Determination of the cutterhead torque for EPB shield tunneling machine. Automation in Construction, 20(8): 1087–1095.
Simpson, P K., 1990. Artificial Neural System: Foundation, Paradigm, Application and Implementations. New York: Pergamon Press.
Wang, L., Gong, G., Shi, H., Yang, H., 2012. A new calculation model of cutterhead torque and investigation of its influencing factors. Science China Technological Sciences, 55(6): 1581–1588.
Yagiz, S., Karahan, H., 2011. Prediction of hard rock TBM penetration rate using particle swarm optimization. International Journal of Rock Mechanics and Mining Sciences, 48(3): 427–433.