Settlement Prediction for Tehran Subway Line-3 via FLAC3D and ANFIS
Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.
[1] Khalili, A., Ahangari, K., Ghaemi M., Zarei H. “Introducing a new criterion for tunnel crown settlement: a case study of Chehel-Chay water conveyance tunnel”. International Journal of Geotechnical Engineering. 2016, pp. 1-11.
[2] Darabi, A., Ahangari, K., Noorzad, A. and Arab, A. 2012. Subsidence estimation utilizing various approaches–A case study: Tehran No. 3 subway line, Tunneling and Underground Space Technology, 31, 117–127.
[3] Yasitli, N. E. “Numerical modeling of surface settlements at the transition zone excavated by New Austrian Tunneling Method and Umbrella Arch Method in weak rock”, Arabian Journal of Geosciences, July 2013, Volume 6, Issue 7, pp 2699–2708
[4] Khalili, A. and Ghaemi, M. 2015. New approach in tunneling construction using the forepooling technique, 11th Iranian and 2nd Regional Tunneling Conference entitled ‘Tunnels and the Future, Tehran, Iran.
[5] Attewell P. B, Yeates J, Selby A. R. Soil movements induced by tunneling and their effects on pipelines and structures (M). London: Blackies and Sons, Ltd, 1986.
[6] Palmstrom A, Stille H. Ground behavior and rock engineering tools for underground excavations (J). Tunneling and Underground Space Technology, 2006, 22: 363−376.
[7] Itasca Consulting Group, Inc. (November 20, 2012) FLAC3D version5, Fast Lagrangian Analysis of Continua in 3Dimensions, User’s manual.
[8] Shi J, Ortigao J, Bai J. Modular neural networks for predicting settlements during tunneling (J). Journal of Geotechnical and Geoenvironmental Engineering, 1998, 124(5): 389−395.
[9] Kim C. Y, Bae G, Hong S, Park C, Moon H, Shin H. Neural network based prediction of ground surface settlements due to tunneling (J). Computers and Geotechnics, 2001, 28(6): 517−547.
[10] Suwansawat S, Einstein H. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling (J). Tunneling and Underground Space Technology, 2006, 21(2): 133−150.
[11] Boubou R, Emeriault F, Kastner R. Artificial neural network application for the prediction of ground surface movements induced by shield tunneling (J). Canadian Geotechnical Journal, 2010, 47(11): 1214−1233.
[12] Ocak I, Seker S. E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes (J). Environmental Earth Sciences, 2013, 70(3): 1263− 1276.
[13] Ahangari K, Moeinossadat S. R, Behnia D. Estimation of tunneling-induced settlement by modern intelligent methods (J). Soils and Foundations, 2015, 55: 737−748.
[14] Neaupane K. M, Adhikari N. Prediction of tunneling-induced ground movement with the multi-layer perceptions (J). Tunneling and Underground Space Technology, 2006, 21(2): 151−159.
[15] Srinivasan, K., Fisher, D., 1995.Machinelearningapproachestoestimating software developmenteffort.IEEETrans.Softw.Eng.21 (2),126–137.
[16] Hou J, Zhang M, and Tu M. Prediction of surface settlements induced by shield tunneling: An ANFIS model (M). London: Taylor & Francis Group, 2009: 551−554.
[17] Jang J. S. R. Anfis: Adaptive-network-based fuzzy inference systems (J). IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665–685.
[18] Kartalopoulos S. V. Understanding neural networks and fuzzy logic (M)// Basic Concepts and Applications. IEEE Press, 1996.
[19] Jang J. S. R, Sun C T. Neuro-fuzzy modeling and control (J). Proceedings IEEE, 1997, 83(3): 378–406.
[20] Jang J. S. R, Sun C. T, Mizutani E. Neuro-fuzzy and soft computing a computational approach to learning and machine intelligence (M). New Jersey: Prentice Hall, 1997.
[21] Behnia D, Moeinossadat S. R, Behnia B, Behnia M, Safari-Gorgi A, Zakerian P. Prediction of settlement in sloping core rockfill dams using soft-computing (J). Research in Civil and Environmental Engineering (RCEE), 2014, 2(2): 55–65.
[22] Negro, A. and B. I. P. Queiroz, 2000. Prediction and performance of soft ground tunnels. Geotechnical Aspects Underground Construction Soft Ground, Balkema, Tokyo, Japan, pp: 409-418.
[23] Farias, M. M. D., A. H. M. Junior and A. P. D. Assis, 2004. Displacement control in tunnels excavated by the NATM: 3-D numerical simulations. Tunnel. Underground Space Technol., 19: 283-293.
[1] Khalili, A., Ahangari, K., Ghaemi M., Zarei H. “Introducing a new criterion for tunnel crown settlement: a case study of Chehel-Chay water conveyance tunnel”. International Journal of Geotechnical Engineering. 2016, pp. 1-11.
[2] Darabi, A., Ahangari, K., Noorzad, A. and Arab, A. 2012. Subsidence estimation utilizing various approaches–A case study: Tehran No. 3 subway line, Tunneling and Underground Space Technology, 31, 117–127.
[3] Yasitli, N. E. “Numerical modeling of surface settlements at the transition zone excavated by New Austrian Tunneling Method and Umbrella Arch Method in weak rock”, Arabian Journal of Geosciences, July 2013, Volume 6, Issue 7, pp 2699–2708
[4] Khalili, A. and Ghaemi, M. 2015. New approach in tunneling construction using the forepooling technique, 11th Iranian and 2nd Regional Tunneling Conference entitled ‘Tunnels and the Future, Tehran, Iran.
[5] Attewell P. B, Yeates J, Selby A. R. Soil movements induced by tunneling and their effects on pipelines and structures (M). London: Blackies and Sons, Ltd, 1986.
[6] Palmstrom A, Stille H. Ground behavior and rock engineering tools for underground excavations (J). Tunneling and Underground Space Technology, 2006, 22: 363−376.
[7] Itasca Consulting Group, Inc. (November 20, 2012) FLAC3D version5, Fast Lagrangian Analysis of Continua in 3Dimensions, User’s manual.
[8] Shi J, Ortigao J, Bai J. Modular neural networks for predicting settlements during tunneling (J). Journal of Geotechnical and Geoenvironmental Engineering, 1998, 124(5): 389−395.
[9] Kim C. Y, Bae G, Hong S, Park C, Moon H, Shin H. Neural network based prediction of ground surface settlements due to tunneling (J). Computers and Geotechnics, 2001, 28(6): 517−547.
[10] Suwansawat S, Einstein H. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling (J). Tunneling and Underground Space Technology, 2006, 21(2): 133−150.
[11] Boubou R, Emeriault F, Kastner R. Artificial neural network application for the prediction of ground surface movements induced by shield tunneling (J). Canadian Geotechnical Journal, 2010, 47(11): 1214−1233.
[12] Ocak I, Seker S. E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes (J). Environmental Earth Sciences, 2013, 70(3): 1263− 1276.
[13] Ahangari K, Moeinossadat S. R, Behnia D. Estimation of tunneling-induced settlement by modern intelligent methods (J). Soils and Foundations, 2015, 55: 737−748.
[14] Neaupane K. M, Adhikari N. Prediction of tunneling-induced ground movement with the multi-layer perceptions (J). Tunneling and Underground Space Technology, 2006, 21(2): 151−159.
[15] Srinivasan, K., Fisher, D., 1995.Machinelearningapproachestoestimating software developmenteffort.IEEETrans.Softw.Eng.21 (2),126–137.
[16] Hou J, Zhang M, and Tu M. Prediction of surface settlements induced by shield tunneling: An ANFIS model (M). London: Taylor & Francis Group, 2009: 551−554.
[17] Jang J. S. R. Anfis: Adaptive-network-based fuzzy inference systems (J). IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665–685.
[18] Kartalopoulos S. V. Understanding neural networks and fuzzy logic (M)// Basic Concepts and Applications. IEEE Press, 1996.
[19] Jang J. S. R, Sun C T. Neuro-fuzzy modeling and control (J). Proceedings IEEE, 1997, 83(3): 378–406.
[20] Jang J. S. R, Sun C. T, Mizutani E. Neuro-fuzzy and soft computing a computational approach to learning and machine intelligence (M). New Jersey: Prentice Hall, 1997.
[21] Behnia D, Moeinossadat S. R, Behnia B, Behnia M, Safari-Gorgi A, Zakerian P. Prediction of settlement in sloping core rockfill dams using soft-computing (J). Research in Civil and Environmental Engineering (RCEE), 2014, 2(2): 55–65.
[22] Negro, A. and B. I. P. Queiroz, 2000. Prediction and performance of soft ground tunnels. Geotechnical Aspects Underground Construction Soft Ground, Balkema, Tokyo, Japan, pp: 409-418.
[23] Farias, M. M. D., A. H. M. Junior and A. P. D. Assis, 2004. Displacement control in tunnels excavated by the NATM: 3-D numerical simulations. Tunnel. Underground Space Technol., 19: 283-293.
@article{"International Journal of Earth, Energy and Environmental Sciences:75927", author = "S. A. Naeini and A. Khalili", title = "Settlement Prediction for Tehran Subway Line-3 via FLAC3D and ANFIS", abstract = "Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.
", keywords = "Settlement, subway line, FLAC3D, ANFIS method.", volume = "11", number = "7", pages = "612-6", }
