Abstract
Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM’s ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling.
Highlights
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Prediction of TBM performance in complex geological conditions.
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Forecasting TBM performance in deep tunnels passing through metamorphic rocks.
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Presenting an empirical model for calculating the TBM performance based on statistical analysis.
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Detailed analysis of fuzzy-based techniques potential for TBM performance prediction.
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Examining the models’ accuracy with several loss functions and statistical indices.
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Abbreviations
- A, B :
-
Fuzzy sets
- Z :
-
Crisp function
- f (X,Y) :
-
Polynomial consisting of input variables
- W i :
-
Firing strength of the ith output
References
Adoko AC, Yagiz S (2019) Fuzzy inference system-based for TBM field penetration index estimation in rock mass. Geotech Geol Eng 37:1533–1553. https://doi.org/10.1007/s10706-018-0706-5
Alber M (2000) Advance rates of hard rock TBM’s and their effect on project economics. Tunn Undergr Space Technol 15(1):55–64. https://doi.org/10.1016/S0886-7798(00)00029-8
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. Tunn Undergr Space Technol 40:46–63. https://doi.org/10.1016/j.tust.2013.09.004
Barla G (2000) Lessons learnt from the excavation of a large diameter TBM tunnel in complex hydrogeological conditions. In: ISRM International Symposium, OnePetro
Barton N (2000) TBM tunnelling in jointed and faulted rock. ISBN 9789058093417. p 184
Benardos A, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19:597–605. https://doi.org/10.1016/j.tust.2004.02.128
Benato A, Oreste P (2015) Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics. Int J Rock Mech Min Sci 74:119–127. https://doi.org/10.1016/j.ijrmms.2014.12.007
Bieniawski ZT, Celada B, Galera JM (2007) TBM excavability: prediction and machine-rock interaction. Proc Rapid Excav Tunn Conf 1118:1118–1130
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. In World Tunnel Congress, India, pp 1509–1517
Bruines P (1988) Neuro-fuzzy modelling of TBM performance with emphasis on the penetration rate. Memoirs of the Centre of Engineering Geology, Delft, p 173
Bruland A (1998) Hard rock tunnel boring. Ph.D. Thesis. Norwegian University of Science and Technology, Trondheim
Chai Y, Jia L, Zhang Z (2009) Mamdani model based adaptive neural fuzzy inference system and its application in traffic level of service evaluation. Sixth Int Conf Fuzzy Syst Knowl Discov 4:555–559
Cheng F, Li J, Zhou L, Lin G (2023) Fragility analysis of nuclear power plant structure under real and spectrum-compatible seismic waves considering soil-structure interaction effect. Eng Struct 280:115684. https://doi.org/10.1016/j.engstruct.2023.115684
Codd EF (1971) Normalized data base structure: a brief tutorial. In: Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) workshop on data description, access and control. P 1–17. https://doi.org/10.1145/1734714.1734716
Date CJ (1999) An introduction to database systems. Addison-Wesley, p 290
Elhaik E (2022) Principal component analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci Rep. https://doi.org/10.1038/s41598-022-14395-4
Farmer IW, Garritty P (1987) Prediction of roadheader cutting performance from fracture toughness considerations. In: 6th ISRM Congress, OnePetro
Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunn Tunn UK 12(6):22-25
Farrokh E, Rostami J (2009) Effect of adverse geological condition on TBM operation in Ghomroud tunnel conveyance project. Tunn Undergr Space Technol 24(4):436–446. https://doi.org/10.1016/j.tust.2008.12.006
Fu Q, Gu M, Yuan J, Lin Y (2022) Experimental study on vibration velocity of piled raft supported embankment and foundation for ballastless high speed railway. Buildings 12(11):1982. https://doi.org/10.3390/buildings12111982
Gao X, Shi M, Song X, Zhang C, Zhang H (2020) Recurrent neural networks for real-time prediction of TBM operating parameters. Autom Constr 98:225–235. https://doi.org/10.1016/j.autcon.2018.11.013
Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Env 73:23–35. https://doi.org/10.1007/s10064-013-0497-0
Gokceoglu C (2022) Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey. SN Applied Sciences 4(1):19. https://doi.org/10.1007/s42452-021-04903-y
Gong Q, 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. https://doi.org/10.1016/j.ijrmms.2008.03.003
Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Space Technol 15(3):259–269. https://doi.org/10.1016/S0886-7798(00)00055-9
Habashy DM, Lebda HI (2022) Comparison between artificial neural network and adaptive neuro-fuzzy inference system for the baryon-to-meson ratios in proton-proton collisions. arXiv preprint arXiv:2209.12709. https://doi.org/10.48550/arXiv.2209.12709
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. Int J Geomech Geoeng 4:287–297. https://doi.org/10.1080/17486020903174303
Hassanpour J, Rostami J, Khamehchiyan M, Bruland A, Tavakoli HR (2010) TBM performance analysis in pyroclastic rocks: a case history of Karaj water conveyance tunnel. Rock Mech Rock Eng 43:427–445. https://doi.org/10.1007/s00603-009-0060-2
Hassanpour J, Rostami J, Zhao J (2011) A new hard rock TBM performance prediction model for project planning. Tunn Undergr Space Technol 26(5):595–603. https://doi.org/10.1016/j.tust.2011.04.004
Howart DF (1994) Database of TBM projects undertaken between 1950 and 1990 and an assessment of associated ground-strength limitations. Tunn Undergr Space Technol 9(2):209–213. https://doi.org/10.1016/0886-7798(94)90032-9
Jang R (1991) Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In: Proceedings of the 9th National Conference on Artificial Intelligence. USA, p 762–767
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Jia S, Dai Z, Zhou Z, Ling H, Yang Z, Qi L, Wang Z, Zhang X, Thanh HV, Soltanian MR (2023) Upscaling dispersivity for conservative solute transport in naturally fractured media. Water Res 235:119844. https://doi.org/10.1016/j.watres.2023.119844
Jolliffe LT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci. https://doi.org/10.1098/rsta.2015.0202
Kahraman S (2007) Historical review of tunnel boring machine (TBM) data. CIM Mag 2(1)
Kalnins A (2022) When does multicollinearity bias coefficients and cause type 1 errors? A reconciliation of Lindner, Puck, and Verbeke (2020) with Kalnins (2018). J Int Bus Stud 53(7):1536–1548. https://doi.org/10.1057/s41267-022-00531-9
Li J, Chen M, Li Z (2022a) Improved soil–structure interaction model considering time-lag effect. Comput Geotech 148:104835. https://doi.org/10.1016/j.compgeo.2022.104835
Li R, Wu X, Tian H, Yu N, Wang C (2022b) Hybrid memetic pretrained factor analysis-based deep belief networks for transient electromagnetic inversion. IEEE Trans Geosci Remote Sens 60:1–20. https://doi.org/10.1109/TGRS.2022.3208465
Li X, Du C, Wang X, Zhang J (2023) Quantitative determination of high-order crack fabric in rock plane. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-023-03319-x
Liu J, Ren J, Guo W (2015) Thrust and torque characteristics based on a new cutter-head load model. Chin J Mech Eng 28(4):801–809. https://doi.org/10.3901/CJME.2015.0504.066
Liu C, Cui J, Zhang Z, Liu H, Huang X, Zhang C (2021) The role of TBM asymmetric tail-grouting on surface settlement in coarse-grained soils of urban area: field tests and FEA modelling. Tunn Undergr Space Technol 111:103857. https://doi.org/10.1016/j.tust.2021.103857
Liu Y, Li J, Lin G (2023a) Seismic performance of advanced three-dimensional base-isolated nuclear structures in complex-layered sites. Eng Struct 289:116247. https://doi.org/10.1016/j.engstruct.2023.116247
Liu C, Peng Z, Cui J, Huang X, Li Y, Chen W (2023b) Development of crack and damage in shield tunnel lining under seismic loading: refined 3D finite element modeling and analyses. Thin-Walled Struct 185:110647. https://doi.org/10.1016/j.tws.2023.110647
Mahmoodzadeh M, Taghizadeh M, Mohammed AH, Ibrahim HH, Samadi H, Mohammadi M, Rashidi S (2022) Tunnel wall convergence prediction using optimized LSTM deep neural network. Geomech Eng 31(6):545–556. https://doi.org/10.12989/gae.2022.31.6.545
Malli T, Mizrak Özfirat P, Yetkin ME, Özfirat MK (2021) Truck selection with the fuzzy-WSM method in transportation systems of open pit mines. Tehnicki Vjesnik-Technical Gazette 28(1):58–64. https://doi.org/10.17559/TV-20190910100025
Mamdani M (1974) The ideology of population control. Concerned Demogr 4(2):13–22 (PMID: 12307029)
Manafiazar A, Khamehchiyan M, Nadiri A, Sharifikia M (2023) Learning simple additive weighting parameters for subsidence vulnerability indices in Tehran plain (Iran) by artificial intelligence methods. Eur J Environ Civ Eng. https://doi.org/10.1080/19648189.2023.2205914
Michael JR (1983) The stabilized probability plot. Biometrika 70(1):11–17
Ozdemir L (1977) Development of theoretical equations for predicting tunnel boreability. Ph.D. thesis. Colorado School of Mines, Colorado, USA
Peng J, Xu C, Dai B, Sun L, Feng J, Huang Q (2022) Numerical investigation of brittleness effect on strength and microcracking behavior of crystalline rock. Int J Geomech. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002529
Ren C, Yu J, Liu S, Yao W, Zhu Y, Liu X (2022) A plastic strain-induced damage model of porous rock suitable for different stress paths. Rock Mech Rock Eng 55(4):1887–1906. https://doi.org/10.1007/s00603-022-02775-1
Ren C, Yu J, Zhang C, Liu X, Zhu Y, Yao W (2023) Micro–macro approach of anisotropic damage: a semi-analytical constitutive model of porous cracked rock. Eng Fract Mech 290:109483. https://doi.org/10.1016/j.engfracmech.2023.109483
Rostami J (1997) Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure. Ph.D. thesis. Colorado School of Mines, Colorado, USA
Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBM. Proceedings of RETC. Boston, MA, p 793–809
Sada SO, Ikpeseni SC (2021) Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Heliyon 7(2):06136. https://doi.org/10.1016/j.heliyon.2021.e06136
Samadi H, Hassanpour J (2021) Developing the empirical models for predicting the EPB operating parameters in strong limestone. Sci Q J Iran Assoc Eng Geol 15(1):29–41
Samadi H, Hassanpour J, Tarigh Azali S (2022) Developing GEP technique for prediction of EPB-TBM performance in limestone strata. TBMDigs 2022, Austria, p 158–164
Sanchez-Torrubia MG, Torres-Blanc C, Escribano-Blanco S (2010) GRAPHs: a learning environment for graph algorithm simulation primed for automatic fuzzy assessment. In: Proceedings of the 10th Koli Calling international conference on computing education research. p 62–67
Sanio HP (1985) Prediction of the performance of disc cutters in anisotropic rock. Int J Rock Mech Min Sci Geomech Abstr 22(3):153–161. https://doi.org/10.1016/0148-9062(85)93229-2
Shao C, Li X, Su H (2013) Performance prediction of hard rock TBM based on extreme learning machine. Intelligent robotics and applications: 6th international conference. Springer Berlin Heidelberg, Busan, South Korea, pp 409–416
Shorack GR, Wellner JA (1986) Empirical processes with applications to statistics. Wiley, pp 248–250
Simpson PK (1990) Artificial neural system: foundation, paradigm, application and implementations. Pergamon Press, New York
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132. https://doi.org/10.1109/TSMC.1985.6313399
Tang H, Yang Y, Li H, Xiao L, Ge Y (2023) Effects of chloride salt erosion and freeze–thaw cycle on interface shear behavior between ordinary concrete and self-compacting concrete. Structures 56:104990. https://doi.org/10.1016/j.istruc.2023.104990
Tortum A, Yayla N, Gökdağ M (2009) The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Syst Appl 36(3):6199–6217. https://doi.org/10.1016/j.eswa.2008.07.032
Wen S, Liu HZ, Zhao LM (2012) Risk analysis on the accident of TBM’s cutterhead jamming caused by collapse of tunnel. Adv Mater Res 446:2246–2250. https://doi.org/10.4028/www.scientific.net/AMR.446-449.2246
Wilk MB, Gnanadesikan R (1968) Probability plotting methods for the analysis for the analysis of data. Biometrika 55(1):1–17. https://doi.org/10.1093/biomet/55.1.1
Xia Y, Shi M, Zhang C, Wang C, Sang X, Liu R, Zhao P, An G, Fang H (2022) Analysis of flexural failure mechanism of ultraviolet cured-in-place-pipe materials for buried pipelines rehabilitation based on curing temperature monitoring. Eng Fail Anal 142:106763. https://doi.org/10.1016/j.engfailanal.2022.106763
Xu H, Zhou J, Asteris PG, Armaghani D, Tahir MM (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9(18):3715. https://doi.org/10.3390/app9183715
Xu Z, Li X, Li J, Xue Y, Jiang S, Liu L, Luo Q, Wu K, Zhang N, Feng Y, Shao M, Jia K, Sun Q (2022a) Characteristics of source rocks and genetic origins of natural gas in deep formations, gudian depression, Songliao Basin, NE China. ACS Earth Space Chem 6(7):1750–1771. https://doi.org/10.1021/acsearthspacechem.2c00065
Xu L, Cai M, Dong S, Yin S, Xiao T, Dai Z, Wang Y, Reza Soltanian M (2022b) An upscaling approach to predict mine water inflow from roof sandstone aquifers. J Hydrol 612:128314. https://doi.org/10.1016/j.jhydrol.2022.128314
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 tunneling machines. Ph.D. thesis. Colorado School of Mines, Colorado, USA, p 289
Yagiz S (2006) TBM performance prediction based on rock properties. Proc Multiphys Coupling Long Term Behav Rock Mech EUROCK 6:663–670
Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23(3):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(3):427–433. https://doi.org/10.1016/j.ijrmms.2011.02.013
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(4–5):808–814. https://doi.org/10.1016/j.engappai.2009.03.007
Yang H, Wang H, Zhou X (2016) Analysis on the rock–cutter interaction mechanism during the TBM tunneling process. Rock Mech Rock Eng 49:1073–1090. https://doi.org/10.1007/s00603-015-0796-9
Yang SQ, Chen M, Fang G, Wang YC, Meng B, Li YH, Jing HW (2018) Physical experiment and numerical modelling of tunnel excavation in slanted upper-soft and lower-hard strata. Tunn Undergr Space Technol 82:248–264. https://doi.org/10.1016/j.tust.2018.08.049
Yang Z, Xu J, Feng Q, Liu W, He P, Fu S (2022) Elastoplastic analytical solution for the stress and deformation of the surrounding rock in cold region tunnels considering the influence of the temperature field. Int J Geomech. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002466
Yao W, Yu J, Liu X, Zhang Z, Feng X, Cai Y (2023) Experimental and theoretical investigation of coupled damage of rock under combined disturbance. Int J Rock Mech Min Sci 164:105355. https://doi.org/10.1016/j.ijrmms.2023.105355
Yetkin ME, Simsir F, Ozfirat MK, Ozfirat PM, Yenice H (2016) A fuzzy approach to selecting roof supports in longwall mining. S Afr J Ind Eng 27(1):162–177
Yin H, Wu Q, Yin S, Dong S, Dai Z, Soltanian MR (2023a) Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest. J Hydrol 616:128813. https://doi.org/10.1016/j.jhydrol.2022.128813
Yin H, Zhang G, Wu Q, Yin S, Soltanian MR, Thanh HV, Dai Z (2023b) A deep learning-based data-driven approach for predicting mining water inrush from coal seam floor using micro-seismic monitoring data. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2023.3300012
Yu J, Zhu Y, Yao W, Liu X, Ren C, Cai Y, Tang X (2021) Stress relaxation behavior of marble under cyclic weak disturbance and confining pressures. Measurement 182:109777. https://doi.org/10.1016/j.measurement.2021.109777
Zhan C, Dai Z, Soltanian MR, de Barros FPJ (2022) Data-Worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework. Water Resour Res. https://doi.org/10.1029/2022WR033241
Zhang X, Ma F, Dai Z, Wang J, Chen L, Ling H, Soltanian MR (2022a) Radionuclide transport in multi-scale fractured rocks: a review. J Hazard Mater 424:127550. https://doi.org/10.1016/j.jhazmat.2021.127550
Zhang X, Wang Z, Reimus P, Ma F, Soltanian MR, Xing B, Zang J, Wang Y, Dai Z (2022b) Plutonium reactive transport in fractured granite: Multi-species experiments and simulations. Water Res 224:119068. https://doi.org/10.1016/j.watres.2022.119068
Zhang K, Wang Z, Chen G, Zhang L, Yang Y, Yao C, Wang J, Yao J (2022c) Training effective deep reinforcement learning agents for real-time life-cycle production optimization. J Petrol Sci Eng 208:109766. https://doi.org/10.1016/j.petrol.2021.109766
Zhou J, Qiu Y, Zhu S, Armaghani DJ, Khandelwal M, Mohamad ET (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Undergr Space 6(5):506–515. https://doi.org/10.1016/j.undsp.2020.05.008
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The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Groups RGP. 2/357/44.
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Samadi, H., Mahmoodzadeh, A., Hussein Mohammed, A. et al. Application of Several Fuzzy-Based Techniques for Estimating Tunnel Boring Machine Performance in Metamorphic Rocks. Rock Mech Rock Eng 57, 1471–1494 (2024). https://doi.org/10.1007/s00603-023-03602-x
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DOI: https://doi.org/10.1007/s00603-023-03602-x