Development of a comprehensive model to evaluate the foam consumption and the advance rate in EPB tunnel boring machine

Document Type : Original Article

Authors

2Ph.D. Student, Islamic Azad University Science and Research Branch

Abstract

In this study, in order to investigate and evaluate the performance of the EPB TBM, the parameters of progress rate and foam consumption for ground improvement have been used. To evaluate the performance of EPB tunnel boring machine in this research, drilling data were used from 4 databases namely Qom metro line one project, Shiraz metro line two project (east and west tunnel), Isfahan metro line two project and Isfahan metro line one project which have been EPB TBM excavated. Based on this database, models based on statistical techniques including regression analysis (simple regression, linear multivariate (MLR) and nonlinear (MNLR) regressions) and support vector machine least squares (LS-SVM) algorithm were developed to evaluate the foam consumption and the advance rate. Then, in order to study more accurately and better the experimental and intelligent models presented to predict the foam consumption and the advance rate, statistical indicators such as the coefficient of determination (R2), the statistical values ​​of the root mean square of the normalized error (NRMSE) and error variance (VAF) for each developed model were calculated using the test data set. Comparison of the calculated values of R2, VAF and NRMSE for the models developed to predict the foam consumption and the advance rate showed that the proposed model based on the LS-SVM algorithm has remarkable accuracy compared to other models, in predicting the amount of foam consumption and the advance rate of the EPB tunnel boring machine. The values of R2, VAF and NRMSE indices for the developed model in the prediction of the amount of foam consumption are 0.945, 94.356 and 0.237 respectively, and in the prediction of the advance rate are equal to 0.741, 74.071 and 0.149, respectively.  Due to the high accuracy of predicting the amount of foam consumption (Qt) and the advance rate (AR), the models which are developed on the LS-SVM algorithm basis, can play a significant role as applied models in the mechanized drilling industry.

Keywords

References

B. Maidl, M, Herrenknecht, L, Anheuser, “Mechanized Shield Tunnelling”, Ernst and Sohn, ISBN: 3- 433-01292-X, 427 p, 1996.## JSCE, Japan Society of Civil Engineers, “Standard Specification for Tunnelling Shield Tunnels”, ISBN: 978-4-8106-0568-6, 270 p, 2006.## H. Copur, 2012. “Mechanical Excavation and Mechanization in Soft Ground” Graduate Class Notes. Istanbul Technical University, Mining Engineering Department, 2012.## S. Psomas, “Properties of foam/sand mixtures for tunnelling applications”, 2001.## D. Peila, A. Picchio, and A. Chieregato, “Earth pressure balance tunnelling in rock masses: Laboratory feasibility study of the conditioning process”, Tunnelling and Underground Space Technology, 35, 55-66, 2013.## M. Khosravi, K. Shahriar, M. Manjezi and M. Khosrotash, “Investigation of the relationship between bitumen injection of soil processing agent and penetration rate in TBM-EPB of Tehran Metro Line 7”, the first Asian conferences and the 9th National Tunnel Conference, 2012, (In persian). ##D. Peila, C, Oggeri, and R. Vinai, “Screw conveyor device for laboratory tests on conditioned soil for EPB tunneling operations.”, Journal of Geotechnical and Geoenvironmental Engineering, 133(12), 1622-1625, 2007.## H. Copur, E. Avunduk, D. Tumac, C, Balci, N. Bilgin, A.S. Mamaghani, S. Tolouei, G. Erkuş, S. Acun, and U. Ates, “Effect of Soil Conditioning on Performance of an EPB TBM: from Laboratory to MahmutbeyMecidiyekoy Metro Tunnels in Istanbul”, ITA-AITES- WORLD TUNNEL CONGRESS, 2018. ##E. Avunduk, and H. Copur, “Effect of Clogging on EPB TBM Performance: A Case Study in Akfirat Waste Water Tunnel, Turkey”, Geotechnical and Geological Engineering, 2019, 37(6), 4789-4801, 2019.## M.A. Grima, P.A. Bruines, and P.N.W. Verhoef, “Modeling tunnel boring machine performance by neuro-fuzzy methods” Tunnelling and underground space technology, 15(3), 259-269, 2000.## A.G. Benardos, and D.C. Kaliampakos, “Modelling TBM performance with artificial neural networks,” Tunnelling and Underground Space Technology, 19(6), 597-605, 2004.## K, Elbaz, S.L. Shen, A. Zhou, D.J. Yuan, and Y.S. Xu, “Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm”, Applied Sciences, 9(4), 780, 2019.## K. Elbaz, S.L. Shen, W.J. Sun, Z. Yin, and A. Zhou, “Prediction model of shield performance during tunneling with AI via incorporating improved PSO into ANFIS”, IEEE Access, 2020.## S. Mokhtari, and M.A. Mooney, “Predicting EPBM advance rate performance using support vector regression modeling”, Tunnelling and Underground Space Technology, 104, 103520, 2020. ##C. Cortes, and V. Vapnik, “Support-vector networks. Machine learning”, 20(3), 273-297, 1995. ## N. Cristianini, and J. Shawe-Taylor, “An introduction to support vector machines and other kernel-based learning methods”, Cambridge university press, 2000.## S. Aboutaleb, M. Behnia, R. Bagherpour, and B. Bluekian, “Using non-destructive tests for estimating uniaxial compressive strength and static Young’s modulus of carbonate rocks via some modeling techniques”, Bulletin of Engineering Geology and the Environment, 77(4), 1717-1728, 2018.##J.A. Suykens, T. Van Gestel, and J. De Brabanter, “Least squares support vector machines,” World scientific, 2002.## A. Seifi, “Developing of expert system to prediction of dailyevapotranspiration by support vector machine and compare results to ANN, ANFIS and experimental method”, M.Sc. Thesis, Department of WaterEngineering, Tarbiat Modarres University, Tehran, Iran, 2020.## M. Yurdakul, and H. Akdas, “Modeling uniaxial compressive strength of building stones using non-destructive test results as neural networks input parameters”, Construction and Building Materials, 47, 1010-1019, 2013.

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