Prediction of Maximum Settlement in EPB Mechanized Twin Tunneling by Using Supervised Combined Artificial Intelligence Model

Authors

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran

Abstract

In this paper, the effective parameters on settlement due to EPB twin tunnelling excavation including face sup-port pressure, back-fill grouting pressure, penetration rate, pitching angle, groundwater level, tunnel depth and soil characteristics (standard penetration test number, soil elastic modulus, dry density, internal friction and cohesion) belonging to a part of the Tabriz metro line 1 twin tunnels in a distance between Qunqa and Gazran stations was se-lected as the intelligent network input data's and designed to optimize artificial intelligence models using artificial neural network, fuzzy logic methods for predicting of maximum surface settlement. Comparing of obtained results with actual measured data's showed that despite of the ability of both artificial intelligence models on estimating the maximum settlements in mechanized excavation, it is still possible to precise the results by applying a combined arti-ficial intelligence model. Therefore, the output of two single models was used as the input of the Nero-fuzzy model and the obtained results (R2 = 0.77 and RMSE = 0.78) indicate a decrease of at least 27% RMSE compared to the in-dividual models.

Keywords

References

ASCE, 2000. Task committee on application of artificial neural networks in hydrology. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering 5(2), 124-137.
ITA/AITES, 2007. Settlements induced by tunneling in Soft Ground. Tunneling and Underground Space Technology 22, 119–149.
Bouayad, D., Emeriault, F., 2017. Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method. Tunneling and Underground Space Technology 68, 142-152.
Camos, C., Spackova, O., Straub, D., Molins, C., 2016. Probabilistic approach to assessing and monitoring settlements caused by tunneling. Tunneling and Underground Space Technology 51, 313–325.
Chen, M. S., Wang, S. W., 1999. Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy sets and systems 103(2), 239-254.
Chiu, S. L., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and fuzzy systems 2(3), 267-278.
Dindarloo, S. R., Siami-Irdemoosa, E., 2015. Maximum surface settlement based classification of shallow tunnels in soft ground. Tunneling and Underground Space Technology 49, 320 – 327.
Hagan, M. T., Demuth. H. B., Bael. M., 1995. Neural Networks Design. IEEE transactions on Neural Networks 5(6), 989-993.
Inanlou, H., Ahanghari, K., 2010. Using artificial neural network as a complement to numerical methods in predicting tunnel settlement in Tabriz Metro line 1. Kharazmi University Journal of Engineering Geology 4 (1), 793-808.
Jafari, H., Pakbaz, M. S., Adib, A., Bagheri-Nya, Kh., 2013. Prediction of ground surface and geomechanical parameters by using of artificial neural network (Case study: Ahwaz Metro). The First Iranian Conference on Geotechnical Engineering, University of Mohaghegh Ardabili. 
Kim, C. Y., Bae, G. J., Hong, S. W., Park, C. H., Moon, H. K., Shin, H. S., 2001. Neural network based prediction of ground surface settlements due to tunneling. Computers and Geotechnics 28(6), 517-547.
Kosko, B., 1992. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Vol. 1, prentice hall
Leca, E., 1989. Analysis of NATM and shield tunneling in soft ground. Ph.D Thesis, Virginia Institute and State University, Blacksburg.
Matsushita, Y., Iwasaki, Y., Hashimoto, T., Imanishi, H., 1995. Behavior of subway tunnel driven by large slurry shield. In Proceedings of the International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground, New Delhi, India, 253-257.
Moeinossadat, S. R., Ahangari, K., Shahriar, K., 2016. Calculation of maximum surface settlement induced by EPB shield tunneling and introducing most effective parameter. Journal of Central South University 23, 3273-3283.
Mohammadi, S. D., Naseri, F., Alipoor, S., 2015. Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran. Bulletin of Engineering Geology and the Environment 74(3), 827-843.
Nadiri, A. A., Fijani, E., Frank T.C., Tsai., Moghaddam, A. A., 2013. Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Journal of Hydroinformatics 15(4), 1474-1490.
Nadiri, A. A., Chitsazan, N., Tsai, F. T. C. Moghaddam, A. A., 2014. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation, Journal of Hydrologic Engineering 19, 520-532.
Nadiri, A.A., Taheri, Z., Khatibi, R., Barzegari, G., Dideban, K., 2018. Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT. Journal of Science Total Environmental 628-629, 1043-1057.
Nadiri, A.A., Gharekhani, M., Khatibi, R., Sadeghfam, S., Asghari Moghaddam, A., 2017 a. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Journal of Science of the Total Environment 574, 691-706.
Nadiri, A., Sedghi, Z., Khatibi, R., Gharekhani, M., 2017 b. Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Journal of Science of the Total Environment 593-594, 75-90.
Nadiri, A.A., Gharekhani, M., Khatibi, R., Moghaddam, A. A., 2017 c. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Journal of Environmental Science and Pollution Research 24(9), 8562-8577.
Nadiri A. A., Shokri S, Tsai FTC, Moghaddam A.A., 2018. Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. Journal of Cleaner Production 180, 539-549.
Nadiri, A.A., Chitsazan, N., Tsai, F. TC., Moghaddam, A.A., 2014. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation. Journal of Hydrologic Engineering 19, 520-532.
Ocak, I., Seker, S. E., 2013. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environmental Earth Sciences 70(3), 1263-1276.
O'Carroll, Jerome B., 2005. A Guide to planning, constructing, and supervising earth pressure balance TBM tunneling. Parsons Brinckerhoff, p.15.
Pantet, A., Kastner, R., Piraud, J., 1993. In situ measurement and calculation of displacement field above slurry shields. Developments in Geotechnical Engineering 74, 443-452.
Peck, R.B., 1969. Deep excavations and tunnelling in soft ground. In: Proc. 7th International Conference on Soil Mechanics and Foundation Engineering, State of the Art Volume, 225-290.
Rezazadeh Anbarani, M., Hajyan, A., Sadeghi, M. M., 2013. Prediction of ground surface settlement due to tunneling by using of fuzzy neural network – case study; Mashhad Metro line, 2nd International Conference on Civil Engineering, Architecture and Urban Sustainable.
Santos, O. J., Celestino, T. B., 2008. Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunnelling and Underground Space Technology 23(5), 481-491.
Show Fang, Y., Wu, C.T., Feng Chen S., Liu, C. 2014. An estimation of subsurface settlement due to shield tunneling. Tunnelling and Underground Space Technology 44, 121-129.
Suwansawat, S., Einstein, H. H., 2006. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology 21(2), 133-150.
Tayfur, G., Nadiri, A. A. and Moghaddam, A. A., 2014. Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation. Water Resource Management 28, 1173-1184.
Zadeh, L. A. Klir, J. G., Yuan, B., 1996. Fuzzy sets, Fuzzy Logic and Fuzzy Systems: Selected Papers, World Scientific, p. 826. 
Advanced Applied Geology
Volume 9, Issue 3
October 2019
Pages 256-271

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