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Research Article

Potential efficacy and application of a new statistical meta based-model to predict TBM performance

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Pages 471-487 | Received 19 Feb 2020, Accepted 15 Jan 2021, Published online: 14 Feb 2021

References

  • Y.L. Zheng, Q.B. Zhang, and J. Zhao, Challenges and opportunities of using tunnel boring machines in mining, Tunnelling Underground Space Technol. 57 (2016), pp. 287–299.
  • M. Cigla, S. Yagiz, and L. Ozdemir, Application of tunnel boring machines in underground mine development. In: International Mining Congress, Ankara, Turkey, 2001.
  • L. Home and O.G. Askilsrud, Tunnel boring machines in mining, in SME Mining Engineering Handbook, P. Darling, ed., third, SME, USA, 2011, pp. 1255–1270.
  • D. Brox, Technical considerations for the application of TBMs for mining projects, Trans. Soc. Mining Metall. Explor. 334 (2013), pp. 498–505.
  • J. Rostami and L. Ozdemir. A new model for performance prediction of hard rock TBM, in Proceedings of RETC, L.D. Bowerman, ed.. Boston, MA, 1993, 793–809.
  • N. Barton, TBM Tunneling in Jointed and Faulted Rock, Balkema, Brookfield, 2000, pp. 173.
  • S. Yagiz , 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, Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA, 2002, pp. 289.
  • S. Yagiz. TBM performance prediction based on rock properties, in EUROCK’06—Multiphysics Coupling and Long Term Behavior in Rock Mechanics, Cotthem, ed., Liege, Belgium, 2006, pp. 663–670.
  • S. Yagiz, Utilizing rock mass properties for predicting TBM performance in hard rock condition, Tunn. Undergr. Sp Technol. 23 (2008), pp. 326–339.
  • S. Yagiz and H. Karahan, Prediction of hard rock TBM penetration rate using particle swarm optimization, Int. J. Rock Mech. Min. Sci. 48 (2011), pp. 427–433.
  • S. Yagiz and H. Karahan, Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass, Int. J. Rock Mech. Min. Sci. 80 (2015), pp. 308–315.
  • L. Ozdemir, Development of theoretical equations for predicting tunnel borability. Ph.D. Thesis, T-1969, Colorado School of Mines, Golden, CO, USA, 1977.
  • O.T. Blindheim, Boreabilty predictions for tunneling. Ph.D. Thesis. Department of geological engineering. The Norwegian Institute of Technology, 1979, pp. 406.
  • D.F. Howarth, W.R. Adamson, and R.J. Berndt, Correlation of model tunnel boring and drilling machine performances with rock properties, Int. J. Rock Mech. Min. Sci. 23 (1986), pp. 171–175.
  • F. Cassinelli, S. Cina, N. Innaurato, R. Mancini, and A. Sampaolo, Power consumption and metal wear in tunnel-boring machines: Analysis of tunnel boring machine operation in hard rock, Tunneling 82 (1982), pp. 73–81.
  • U. Aeberli and H. Wanner, On the influence of geologic conditions at the application of tunnel boring machines. In: Proc. 3rd Int. Cong., Int. Assoc. Eng. Geol., Madrid, section III, 2, Madrid, Spain, (1978), pp. 7–14.
  • H.P. Sanio, Prediction of the performance of disc cutters in anisotropy rocks, Int. J. Rock Mech. Min. Sci. 22 (1985), pp. 153–161.
  • P.J. Tarkoy, Rock hardness index properties and geotechnical parameters for predicting tunnel boring machine performance. Ph.D. Thesis, University of Illinois at Urbana-Champaign, IL, 1975, pp. 326.
  • P. Graham, Rock exploration for machine manufacturers, in Exploration for Rock Engineering, Z.T. Bieniawski, ed., Balkema, Rotterdam, 1976, pp. 173–180.
  • I. Farmer and N. Glossop, Mechanics of disc cutter penetration, Tunn Tunn 12 (1980), pp. 22–25.
  • W. Bamford, Rock test indices are being successfully correlated with tunnel boring machine performance. In: Proceedings of the 5th Australian tunneling conference, Melbourne, Australian, 1984, pp 9–22.
  • J. Rostami, Development of a force estimation model for rock fragmentation with disc cutters through theoretical and physical measurement of crushed zone pressure. Ph.D Dissertation, Colorado School of Mines, Golden, Colorado, 1997.
  • A. Bruland, Hard rock tunnel boring, Ph.D. Thesis. Norwegian University of Science and Technology, 1998.
  • G. Dollinger, H. Handewith, and C. Breeds, Use of the punch test for estimating TBM performance, Tunn. Undergr. Sp Technol. 13 (1998), pp. 403–408.
  • M. Sapigni, M. Berti, E. Bethaz, A. Busillo, and G. Cardone, TBM performance estimation using rock mass classifications, Int. J. Rock Mech. Min. Sci. 39 (2002), pp. 771–788.
  • R. Ribacchi and A.L. Fazio, Influence of rock mass parameters on the performance of a TBM in a gneissic formation (Varzo Tunnel), Rock Mech. Rock Eng. 38 (2005), pp. 105–127.
  • J. Hassanpour, J. Rostami, M. Khamehchiyan, and A. Bruland, Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: A case history of Nowsood water conveyance tunnel, GeoMech. GeoEng. Int. J. 4 (2009), pp. 287–297.
  • K. Gehring, The influence of TBM design and machine features on performance and tool wear in rock, Der Einfluss Von TBM-Konstruktion Und Maschineneigenschaften Auf Leistung Und Werkzeugverbrauch in Gestein Geomech Tunnel 2 (2009), pp. 140–155.
  • J.K. Hamidi, K. Shahriar, B. Rezai, and J. Rostami, Performance prediction of hard rock TBM using rock mass rating (RMR) system, Tunn. Undergr. Sp Technol. 25 (2010), pp. 333–345.
  • J. Hassanpour, J. Rostami, and J. Zhao, A new hard rock TBM performance prediction model for project planning, Tunn. Undergr. Sp Technol. 26 (2011), pp. 595–603.
  • E. Farrokh, J. Rostami, and C. Laughton, Study of various models for estimation of penetration rate of hard rock TBMs, Tunn. Undergr. Sp Technol. 30 (2012), pp. 110–123.
  • M.R. Moradi and M.A.E. Farsangi, Application of the risk matrix method for geotechnical risk analysis and prediction of the advance rate in rock TBM tunneling, Rock Mech. Rock Eng. 47 (2014), pp. 1951–1960.
  • D. Jahed Armaghani, E.T. Mohamad, M.S. Narayanasamy, et al, Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition, Tunn. Undergr. Sp Technol. 63 (2017), pp. 9–43.
  • D. Jahed Armaghani, R.S. Faradonbeh, E. Momeni, et al., Performance prediction of tunnel boring machine through developing a gene expression programming equation, Eng. Comput. (2017), doi:10.1007/s00366-017-0526-x
  • M. Koopialipoor, H. Tootoonchi, D. Jahed Armaghani, et al., Application of deep neural networks in predicting the penetration rate of tunnel boring machines, Bull. Eng. Geol. Environ. 78 (2019c), pp. 6347–6360. doi:10.1007/s10064-019-01538-7
  • H. Xu, J. Zhou, G.P. Asteris, et al, Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate, Appl. Sci. 9 (2019), pp. 3715.
  • J. Zhou, J.Y. Qiu, S. Zhu, D.J. Armaghani, M. Khandelwal, and E.T. Mohamad, Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization, Underground Space (2020). doi:10.1016/j.undsp.2020.05.008.
  • J. Zhou, B.Y. Bejarbaneh, D.J. Armaghani, et al, Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques, Bull. Eng. Geol. Environ. 79 (2020), pp. 2069–2084.
  • M.A. Grima, P. Bruines, and P. Verhoef, Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunn. Undergr. Sp Technol. 15 (2000), pp. 259–269.
  • A. Benardos and D. Kaliampakos, A methodology for assessing geotechnical hazards for TBM tunnelling—illustrated by the Athens Metro, Greece, Int. J. Rock Mech. Min. Sci. 41 (2004), pp. 987–999.
  • A. Benardos and D. Kaliampakos, Modelling TBM performance with artificial neural networks, Tunn. Undergr. Sp Technol. 19 (2004), pp. 597–605.
  • Z. Zhao, Q. Gong, Y. Zhang, and J. Zhao, Prediction model of tunnel boring machine performance by ensemble neural networks, GeoMech. GeoEng. Int. J. 2 (2007), pp. 123–128.
  • O. Acaroglu, L. Ozdemir, and B. Asbury, A fuzzy logic model to predict specific energy requirement for TBM performance prediction, Tunn. Undergr. Sp Technol. 23 (2008), pp. 600–608.
  • S. Yagiz, Assessment of brittleness using rock strength and density with punch penetration test, Tunn. Undergr. Sp Technol. 24 (2009), pp. 66–74.
  • S. Torabi, H. Shirazi, H. Hajali, and M. Monjezi, Study of the influence of geotechnical parameters on the TBM performance in Tehran-Shomal highway project using ANN and SPSS, Arab. J. Geosci. 6 (2013), pp. 1215–1227.
  • S. Mahdevari, K. Shahriar, S. Yagiz, and M.A. Shirazi, A support vector regression model for predicting tunnel boring machine penetration rates, Int. J. Rock Mech. Min. Sci. 72 (2014), pp. 214–229.
  • E. Ghasemi, S. Yagiz, and M. Ataei, Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic, Bull. Eng. Geol. Environ. 73 (2014), pp. 23–35.
  • A. Salimia, C. Moormanna, T. Singh, and P. Jainc, TBM performance prediction in rock tunneling using various artificial intelligence algorithms. In: 11th Iranian and 2nd regional tunnelling conference Iran, Tehran, Iran, 2015.
  • A. Salimi, J. Rostami, and C. Moormann, Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms, Tunnelling Underground Space Technol. 92 (2019), pp. 103046.
  • D. Jahed Armaghani, M. Koopialipoor, A. Marto, and S. Yagiz, Application of several optimization techniques for estimating TBM advance rate in granitic rocks, J. Rock Mech. Geotech. Eng. 11 (2019), pp. 779–789.
  • M. Shi, L. Zhang, W. Sun, and X. Song, A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine, Knowledge Based Syst. 182 (2019), pp. 104859.
  • A.C. Adoko, C. Gokceoglu, and S. Yagiz, Bayesian prediction of TBM penetration rate in rock mass, Eng. Geol. 226 (2017), pp. 245–256.
  • S. Yagiz, New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass, Arabian J. Geosci. 10 (2017), pp. 33.
  • S. Yagiz, C. Gokceoglu, E. Sezer, and S. Iplikci, Application of two non-linear prediction tools to the estimation of tunnel boring machine performance, Eng. Appl. Artif. Intell. 22 (2009), pp. 808–814.
  • M. Zare Naghadehi, M. Samaei, M. Ranjbarnia, and V. Nourani, State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming, Measurement 126 (2018), pp. 46–57.
  • J. Gholamnejad and N. Tayarani, Application of artificial neural networks to the prediction of tunnel boring machine penetration rate, Min. Sci. Technol. 20 (2010), pp. 727–733.
  • K. Oraee, M.T. Khorami, and N. Hosseini, Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (anfis). In: Proceeding of SME Annual Meeting & Exhibit, From the Mine to the Market, Now It’s Global, Seattle, WA, USA, 2012, pp. 297–302.
  • J. Zhou, E. Li, F. Gong, M. Wang, and Q. Qiao, Development of random forests and Cubist models for predicting TBM penetration rate in hard rock condition. In: The 15th Annual Meeting of Chinese rock mechanics and Engineering (China Rock 2018). Beijing, China, 2018, pp. 10.
  • B. Keshtegar, M.F. Allawi, H.A. Afan, and A. El-Shafie, Optimized river stream-flow forecasting model utilizing high-order response surface method, Water Resour. Manage. 30 (2016), pp. 3899–3914.
  • B. Keshtegar, Limited conjugate gradient method for structural reliability analysis, Eng. Comput. 33 (2017), pp. 621–629.
  • B. Keshtegar, B. Mansour, C.W. Fei, C. Lu, O. Taylan, and D.C. Thai, Multi-extremum-modified response basis model for nonlinear response prediction of dynamic turbine blisk, Eng. Comput. (2021). https://doi.org/10.1007/s00366-020-01273-8
  • M. Xiao, J. Zhang, and L. Gao, A system active learning Kriging method for system reliability-based design optimization with a multiple response model, Reliab. Eng. Syst. Saf. 199 (2020), pp. 106935.
  • S.P. Zhu, B. Keshtegar, S. Chakraborty, and N.T. Trung, Novel probabilistic model for searching most probable point in structural reliability analysis, Comput. Methods Appl. Mech. Eng. 366 (2020), pp. 113027.
  • Y. Zhang, L. Gao, and M. Xiao, Maximizing natural frequencies of inhomogeneous cellular structures by Kriging-assisted multiscale topology optimization, Comput. Struct. 230 (2020), pp. 106197.
  • J. Zhang, M. Xiao, L. Gao, and S. Chu, A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities, Comput. Methods Appl. Mech. Eng. 344 (2019), pp. 13–33.
  • L. Gao, M. Xiao, X. Shao, P. Jiang, L. Nie, and H. Qiu, Analysis of gene expression programming for approximation in engineering design, Struct. Multidiscip. Optim. 46 (2012), pp. 399–413.
  • S.P. Zhu, B. Keshtegar, K. Tian, and N.T. Trung, Optimization of load-carrying hierarchical stiffened shells: Comparative survey and applications of six hybrid heuristic models, Arch. Comput. Methods Eng. (2021). https://doi.org/10.1007/s11831-021-09528-3
  • J.H. Friedman, Multivariate adaptive regression splines, Ann. Stat. (1991), pp. 1–67.
  • J. Zhang, M. Xiao, and L. Gao, A new method for reliability analysis of structures with mixed random and convex variables, Appl. Math. Model. 70 (2019), pp. 206–220.
  • W. Zhang and A.T.C. Goh, Multivariate adaptive regression splines for analysis of geotechnical engineering systems, Comput. Geotech. 48 (2013), pp. 82–95.
  • D. Xiu and G.E. Karniadakis, The Wiener–Askey polynomial chaos for stochastic differential equations, SIAM J. Sci. Comput. 24 (2002), pp. 619–644.
  • C.W. Fei, H. Li, H.-T. Liu, C. Lu, and B. Keshtegar, Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship, Aerosp. Sci. Technol. 103 (2020), pp. 105906.
  • J. Zhang, M. Xiao, L. Gao, and S. Chu, Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine, Comput.-Aided Civ. Infrastruct. Eng. 34 (2019), pp. 991–1009.
  • N. Wiener, The homogeneous chaos, Am. J. Math. 60 (1938), pp. 897–936.
  • S. Wang, G. Huang, B. Baetz, and W. Huang, A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment, J. Hydrol. 530 (2015), pp. 716–733.
  • R. Lasota, R. Stocki, P. Tauzowski, and T. Szolc, Polynomial chaos expansion method in estimating probability distribution of rotor-shaft dynamic responses, Bull. Pol. Acad. Sci. Tech. Sci. 63 (2015), pp. 413–422.
  • G. Blatman and B. Sudret, An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Probab. Eng. Mech. 25 (2010), pp. 183–197.
  • J.C. He, S.P. Zhu, D. Liao, and X.P. Niu, Probabilistic fatigue assessment of notched components under size effect using critical distance theory, Eng. Fract. Mech. 103 (2020), pp. 107150.
  • B. Keshtegar, D. Meng, M.E.A. Ben Seghier, M. Xiao, N.T. Trung, and D.T. Bui, A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization, Eng. Comput. (2020). doi:10.1007/s00366-019-00907-w.
  • D. Jahed Armaghani, P.G. Asteris, S.A. Fatemi, M. Hasanipanah, et al, On the use of neuro-swarm system to forecast the pile settlement, Appl. Sci. 10(2020) (1904). https://doi.org/10.3390/app10061904
  • D. Jahed Armaghani and P.G. Asteris, A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength, Neural. Comput. Applic. (2020). doi:10.1007/s00521-020-05244-4.
  • J. Zhou, X. Li, and X. Shi, Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines, Saf. Sci. 50, 4 (2012), pp. 629–644. doi:10.1016/j.ssci.2011.08.065
  • J. Zhou, X. Li, and H.S. Mitri, Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction, Nat. Hazards 79 (2015), pp. 291–316.
  • J. Zhou, X. Li, and H.S. Mitri, Classification of rockburst in underground projects: Comparison of ten supervised learning methods, J. Comput. Civil Eng. 30, 5 (2016), pp. 04016003. doi:10.1061/(ASCE)CP.1943-5487.0000553
  • J. Zhou, X. Li, and H.S. Mitri, Evaluation method of rockburst: State-of-the-art literature review, Tunnelling Underground Space Technol. 81 (2018), pp. 632–659. doi:10.1016/j.tust.2018.08.029
  • H. Nikafshan Rad, M. Hasanipanah, M. Rezaei, and A.L. Eghlim, Developing a least squares support vector machine for estimating the blast-induced flyrock, Eng. Comput. 34 (2019), pp. 709–717.
  • J. Zhou, E. Li, S. Yang, M. Wang, X. Shi, S. Yao, and H.S. Mitri, Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories, Saf. Sci. 118 (2019), pp. 505–518. doi:10.1016/j.ssci.2019.05.046
  • J. Huang, T. Duan, Y. Zhang, J. Liu, J. Zhang, and Y. Lei, Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random forest model, Adv. Civ. Eng. 2020 (2020), pp. 8863181.
  • M. Hasanipanah, B. Keshtegar, D.K. Thai, and N.T. Troung, An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting, Eng. Comput. (2020). doi:10.1007/s00366-020-01105-9.
  • Y. Sun, Y. G. Li, and J. Zhang, Developing hybrid machine learning models for estimating the unconfined compressive strength of jet grouting composite: A comparative study, Appl. Sci. 10 (2020), pp. 1612.
  • M. Hasanipanah, W. Zhang, D.J. Armaghani, and H.N. Rad, The potential application of a new intelligent based approach in predicting the tensile strength of rock, IEEE Access 8 (2020), pp. 57148–57157.
  • M. Hasanipanah and H.B. Amnieh, Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak, Eng. Comput. (2020). doi:10.1007/s00366-019-00919-6.
  • M. Xiao, J. Zhang, L. Gao, S. Lee, and A.T. Eshghi, An efficient Kriging-based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability, Struct. Multidiscip. Optim. 59 (2019), pp. 2077–2092.
  • C.J. Willmott, On the validation of models, Phys. Geogr. 2 (1981), pp. 184–194.
  • J.E. Nash and J.V. Sutcliffe, River flow forecasting through conceptual models part I — A discussion of principles, J. Hydrol. 10 (1970), pp. 282–290.
  • Y. Yang and O. Zang, A hierarchical analysis for rock engineering using artificial neural networks, Rock Mech. Rock Eng. 30 (1997), pp. 207–222.

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