Investigation of the Effect of Different Parameters on the Penetration Rate of Earth Pressure Balance Boring Machine using Fuzzy and Neuro-Fuzzy Methods, and Metaheuristic Algorithms (A Case Study: Tabriz Metro Line 2)

Document Type : Research Article

Authors

Dept. of Mining, Sahand University of Technology, Iran

10.29252/anm.2020.13347.1430

Abstract

One of the most widely used methods for the excavation of metro tunnels is mechanized excavation using an earth pressure balance (EPB) boring machine. Predicting the penetration rate of the boring machine can significantly reduce costs in mechanized excavation. Geological and geotechnical factors, machine specifications, and operational parameters can be influential on the penetration rate of the machine. Important geotechnical factors include cohesion, friction angle, and soil shear modulus. Among the important machine parameters, the thrust force of the jacks, the torque, and the rotational speed of the cutter head can be mentioned. In this study, after analyzing the main component, eliminating the outlier data, and normalizing the data, by considering the geotechnical factors and various parameters of the mechanized boring machine, the penetration rate of the EPB machine in the Tabriz metro line 2 tunnel has been predicted. For this purpose, linear regression methods, fuzzy logic using Mamdani and Sugeno algorithms, neuro-fuzzy method, and metaheuristic algorithms were used. To validate each model, statistical indices of the coefficient of determination (), root mean squares error (RMSE), and performance indicator (VAF) were used. The results of the studies showed that the neuro-fuzzy method has a better prediction of the penetration rate in comparison to other methods. Also, the results of the sensitivity analysis revealed that the cutter head torque had the greatest effect on the penetration rate of the EPB machine.

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[1] Shi, H., Yang, H., Gong, G., & Wang, L. (2011). Determination of the cutter head torque for EPB shield tunneling machine. Automation in Construction, 20, 1087-1095.
[2] Bakhshandeh Amnieh, H., Zamzam, M. S., Moosavi, S. E., & Tarigh Azali, S. (2014). Selection of the most appropriate soil conditioning set in mechanized boring of Tehran metro line 7 tunnel. Tunneling & Underground Space Engineering (TUSE), 2(2), 145-154.
[3] Farmer, I. W., & Glossop, N. H. (1980). Mechanics of disc cutter penetration. Tunnels and Tunnelling International, 12(6), 622-625.
[4] Berke, L., & Hajela, P. (1991). Application of Neural Networks in Structural Optimization. NATO-DFG Advanced Study Institute on optimization of large structural systems, 2, 731-745.
[5] Barton, N. (2000). TBM Tunneling in Jointed and Faulted Rock, Balkema Publishers, Rotterdam.
[6] Sapigni, M., Berti, M., Bethaz, E., Busillo, A. & Cardone, G. (2002). TBM performance estimation using rock mass classifications. International Journal of Rock Mechanics and Mining Sciences, 39, 771-788.
[7] Jahed Armaghani, D., Koopialipoor, M., Marto, A., & Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11, 779-789.
[8] Faramarzi, L., Kheradmandian, A., & Azhari, A. (2020). Evaluation and optimization of the effective parameters on the shield tbm performance: torque and thrust-using discrete element method (DEM). Geotechnical and Geological Engineering, 38, 2745-2759.
[9] Zhao, J., Gong, Q.M., & Eisensten, Z. (2007). Tunnelling through a frequently changing and mixed ground: A case history in Singapore. Tunnelling and Underground Space Technology, 22, 388-400.
[10] Elbaz K, Shen, S. L., Zhou, A., Yuan, D. J., & Xu, Y. S. (2019). Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm. Applied Sciences, 9(4), 1-17.
[11] Chou, H. S., Yang, C. Y., Hsieha, B. J., & Chang, S. S. (2001). A study of liquefaction related damages on shield tunnels. Tunnelling and Underground Space Technology, 16,185-193.
[12] Ball, R. P. A, Young, D. Y., Isaacson, J., Champa, J., & Gause, C. (2009). Research in soil conditioning for EPB tunneling through difficult soils. In: Rapid excavation and tunneling conference, Las Vegas, USA, 320–333
[13] Zumsteg, R., Plotze, M., & Puzrin, A. M. (2013). Reduction of the clogging potential of clays: new chemical applications and novel quantification approaches. Geotechnique, 63(4), 276-286.
[14] Alavi Gharahbagh, E., Rostami, J., & Talebi, K. (2014).  Experimental study of the effect of conditioning on abrasive wear and torque requirement of full face tunneling machines. Tunnelling and Underground Space Technology, 41, 127-136.
[15] Zhao, B., Liu, D., & Jiang, B. (2018). Soil conditioning of waterless sand–pebble stratum in EPB tunnel construction. Geotechnical and Geological Engineering, 36, 2495-2504.
[16] Mohammadi, S. D., Firuzi, M., & Asghari Kaljahi, E. (2016). Geological-geotechnical risk in the use of EPB-TBM, case study: Tabriz Metro, Iran. Bulletin of Engineering Geology and the Environment, 75, 1571-1583.
[17] Centis, S., & Giacomin, G. (2004). EPB tunnelling in highly variable ground–the experience of Oporto Light Metro. Tunnelling and Underground Space Technology, 19, 358.
[18] Carrieri, G., Fornari, E., Guglielmetti, V., & Crova, R. (2006). Torino metro line 1: use of three TBM-EPBs in very coarse grained soil. Tunnelling and Underground Space Technology, 21,274-275.
[19] Song, X., Liu, J., & Guo, W. (2010). A cutter head torque forecast model based on multivariate nonlinear regression for EPB shield tunneling. Proc. Int. Conf. Artif. Intell. Comput. Intell., 104-108.
[20] Toth, A., & Zhao, J. (2013). Evaluation of EPB TBM performance in mixed ground conditions. In: World Tunnel Congress, Switzerland-Geneva, 1149-1156.
[21] Barzegari, G., Uromeihy, A., & Zhao, J. (2014). EPB tunneling challenges in bouldery ground: a new experience on the Tabriz metro line 1, Iran. Bulletin of Engineering Geology and the Environment, 73, 429-440.
[22] Namli, M., & Bilgin, N. (2017). A model to predict daily advance rates of EPB-TBMs in a complex geology in Istanbul. Tunnelling and Underground Space Technology, 62, 43-52.
[23] Avunduk, E., & Copur, H. (2018). Empirical modeling for predicting excavation performance of EPB TBM based on soil properties. Tunnelling and Underground Space Technology, 71, 340-353.
[24] Massinas, S., Prountzopoulos, G. K., Bhardwaj, V., Saxena, A., Clark, J., & Sakellariou, M. G. (2018). Design aspects of under-passing a city’s heritage landmark with EPB machines under low overburden; The case of Chandpole Gate in Jaipur Metro, India. Geotechnical and Geological Engineering, 36, 3683-3705.
[25] Tzamos, S., & Sofianos, A. I., (2006). Extending the Q systems prediction of support in tunnels employing fuzzy logic and extra parameters. International Journal of Rock Mechanics & Mining Sciences, 43(6), 938-949.
[26] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
[27] Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
[28] Acaroglu, O., Ozdemir, L., & Asbury, B. (2008). A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnelling and Underground Space Technology, 23(5), 600-608.
[29] Wang, Y., & Chen, Y. (2014). A comparison of Mamdani and Sugeno fuzzy inference systems for traffic flow prediction. Journal of computers, 9(1), 12-21.
[30] Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York.
[31] Imensazen Consultant Engineers Institute (2009-2018). Tunnel Quality Control Reports and Route of Tabriz Metro line 2.
[32] Asgharzadeh Dizaj, M. (2019). Predicting Performance of Tunnel Boring Machine Using Fuzzy Logic Intelligent Method (Case Study: Tabriz Metro line 2), MSc Thesis, Sahand University of Technology.
[33] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95- International Conference on Neural Networks, Perth, Australia.
[34] Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan.
[35] Dorigoa, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3), 243-278.
[36] Darbor, M., Faramarzi, L., & Sharifzadeh, M. (2019). Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network. Bulletin of Engineering Geology and the Environment, 78(3), 1501-1513.