Abstract
With the continuous development and progress of technology, the emergence of tunnel boring machine (TBM) provides a safer, more effective and cheaper construction method for tunnel digging.
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References
Adoko AC, Gokceoglu C, Yagiz S (2017) Bayesian prediction of TBM penetration rate in rock mass. Eng Geol 226:245–256. https://doi.org/10.1016/j.enggeo.2017.06.014
Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259–269. https://doi.org/10.1016/S0886-7798(00)00055-9
Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009
Barton N (2000) TBM tunnelling in jointed and fault.pdf. Balkema
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Bruland A (1999) Hard rock tunnel boring advance rate and cutter wear. Trondheim Nor Inst Technol 3:Project report 1B-98, NTNU Trondheim
Chen W, Hong H, Panahi M et al (2019) Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with whale optimization algorithm (WOA) and grey wolf optimizer (GWO). Appl Sci 9:. https://doi.org/10.3390/APP9183755
Drillability predictions in hard rock tunnelling: Blindheim, O T In: Tunnelling \"79, Proceedings of the 2nd international symposium, London, 12–16 March 1979, P284–289. Publ London, IMM, 1979
Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science. IEEE, pp 39–43
Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46:8–18. https://doi.org/10.1016/j.ijrmms.2008.03.003
Hassanpour J, Rostami J, Khamehchiyan M, Bruland A (2009) Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of nowsood water conveyance tunnel. Geomech Geoengin 4:287–297. https://doi.org/10.1080/17486020903174303
Hou S, Liu Y, Zhang K (2020) Prediction of TBM tunnelling parameters based on IPSO-BP hybrid model. Yanshilixue Yu Gongcheng Xuebao/Chinese J Rock Mech Eng 39:1648–1657. https://doi.org/10.13722/J.CNKI.JRME.2019.1084
Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405. https://doi.org/10.1007/s00521-015-2072-z
Kahraman S, Bilgin N, Feridunoglu C (2003) Dominant rock properties affecting the penetration rate of percussive drills. Int J Rock Mech Min Sci 40:711–723. https://doi.org/10.1016/S1365-1609(03)00063-7
Koopialipoor M, Tootoonchi H, Jahed Armaghani D et al (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
Li SH, Wu LZ, Luo XH (2020) A novel method for locating the critical slip surface of a soil slope. Eng Appl Artif Intell 94. https://doi.org/10.1016/J.ENGAPPAI.2020.103733
Li X, Liu X, Li CZ et al (2019) Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement. Struct Heal Monit 18:715–724. https://doi.org/10.1177/1475921718767935
Li XF, Li HB, Liu YQ et al (2016) Numerical simulation of rock fragmentation mechanisms subject to wedge penetration for TBMs. Tunn Undergr Sp Technol 53:96–108. https://doi.org/10.1016/J.TUST.2015.12.010
Liu X, Zhou S, Xu M, et al (2018) Analysis of rock fragmentation process with double circle cutters in mixed ground. Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal Basic Sci Eng 26:357–370. https://doi.org/10.16058/j.issn.1005-0930.2018.02.013
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. https://doi.org/10.1016/j.ijrmms.2014.09.012
Mahmoodzadeh A, Mohammadi M, Daraei A et al (2020) Forecasting maximum surface settlement caused by urban tunneling. Autom Constr 120. https://doi.org/10.1016/j.autcon.2020.103375
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Reshef DN, Reshef YA, Finucane HK, et al (2011) Detecting novel associations in large data sets. Science (80-) 334:1518–1524. https://doi.org/10.1126/science.1205438
Sapigni M, Berti M, Bethaz E et al (2002) TBM performance estimation using rock mass classifications. Int J Rock Mech Min Sci 39:771–788. https://doi.org/10.1016/S1365-1609(02)00069-2
Tan H, Ji HG, Zeng ZY, Liu ZQ (2020) Optimal drilling pressure of cone-tipped cutters based on characteristic size of hard and brittle rocks. Yantu Gongcheng Xuebao/Chinese J Geotech Eng 42:782–789. https://doi.org/10.11779/CJGE202004023
Xiong F, Hu Z, Ren X, Zhang P (2017) Matlab-based bp neural network applied to the prediction of tbm advance rate. Mod Tunn Technol 54:101–107. https://doi.org/10.13807/J.CNKI.MTT.2017.05.014
Xu H, Zhou J, Asteris PG, et al (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9. https://doi.org/10.3390/app9183715
Xu S, Niu R (2018) Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Comput Geosci 111:87–96. https://doi.org/10.1016/j.cageo.2017.10.013
Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Sp Technol 23:326–339. https://doi.org/10.1016/j.tust.2007.04.011
Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427–433. https://doi.org/10.1016/j.ijrmms.2011.02.013
Yan CB, Wang HJ, Yang JH et al (2021) Predicting TBM penetration rate with the coupled model of partial least squares regression and deep neural network. Yantu Lixue/Rock Soil Mech 42:519–528. https://doi.org/10.16285/j.rsm.2020.0164
Yan C, Jiang X (2020) Prediction model of tbm net advance rate based on parameters of rock mass and tunnelling. Mod Tunn Technol 57:26–33. https://doi.org/10.13807/J.CNKI.MTT.2020.02.004
Zafar A, Shah S, Khalid R, et al (2017) A meta-heuristic home energy management system. In: Proceedings—31st ieee international conference on advanced information networking and applications workshops, WAINA 2017. pp 244–250
Zhang W, Tang L, Li H et al (2020) Probabilistic stability analysis of Bazimen landslide with monitored rainfall data and water level fluctuations in Three Gorges Reservoir, China. Front Struct Civ Eng 14:1247–1261. https://doi.org/10.1007/s11709-020-0655-y
Zhang Z, Gao Q (2018) Theory and Application of Full Face Rock Tunnel Boring Machine Cutterhead Bending Model. Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal Basic Sci Eng 26:1121–1129. https://doi.org/10.16058/J.ISSN.1005-0930.2018.05.018
Zhu M, Zhu H, Wang X, Cheng P (2020) Study on CART-based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rockmasses. Yanshilixue Yu Gongcheng Xuebao/Chinese J Rock Mech Eng 39:1860–1871. https://doi.org/10.13722/J.CNKI.JRME.2019.0924
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Zhang, W., Zhang, Y., Gu, X., Wu, C., Han, L. (2022). Prediction for TBM Penetration Rate Using Four Hyperparameter Optimization Methods and RF Model. In: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience. Springer, Singapore. https://doi.org/10.1007/978-981-16-6835-7_8
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