Top

Published in:

2022 | OriginalPaper | Chapter

8. Prediction for TBM Penetration Rate Using Four Hyperparameter Optimization Methods and RF Model

Authors : Prof. Wengang Zhang, Assoc. Prof. Yanmei Zhang, Dr. Xin Gu, Dr. Chongzhi Wu, Dr. Liang Han

Published in: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Prediction of Undrained Shear Strength Using XGBoost and RF Based on BO

next chapter What We Have Learnt from the Applications

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.2017.06.014CrossRef

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)00055-9CrossRef

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.2016.12.009CrossRef

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:1010933404324CrossRefMATH

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.2008.03.003CrossRef

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/10.1080/17486020903174303CrossRef

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-zCrossRef

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)00063-7CrossRef

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/S10064-019-01538-7CrossRef

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/10.1177/1475921718767935CrossRef

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.2015.12.010CrossRef

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.2014.09.012CrossRef

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 12:2825–2830MathSciNetMATH

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)00069-2CrossRef

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.2017.10.013CrossRef

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.2007.04.011CrossRef

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.2011.02.013CrossRef

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-yCrossRef

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

Title: Prediction for TBM Penetration Rate Using Four Hyperparameter Optimization Methods and RF Model
Authors: Prof. Wengang Zhang
Assoc. Prof. Yanmei Zhang
Dr. Xin Gu
Dr. Chongzhi Wu
Dr. Liang Han
Publisher: Springer Singapore
Book: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience
Print ISBN: 978-981-16-6834-0

Electronic ISBN: 978-981-16-6835-7

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-981-16-6835-7_8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"