Open Access

The Effect of Loading Type on the Amount of Effect of Loading on the Surface Settlement During Forepoling Tunnel Excavation in Different Geotechnical Conditions

   | Jun 05, 2020

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Ambrožič, T., Turk, G., 2003. Prediction of subsidence due to underground mining by artificial neural networks. Computers & Geosciences, 29(5), pp. 627-637.10.1016/S0098-3004(03)00044-XSearch in Google Scholar

Gueno Consulting Engineers (GCE) - Ben Project Structural Consulting Engineers, January 2012.Search in Google Scholar

Habibi, A., 2010. Crossing the collapse aquifer area (Emamzadeh Hashem (AS) Episode One Variant Project, Khatam Al-Anbia Construction Headquarter Publication, Qorb Nuh (AS), first edition.Search in Google Scholar

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(6-7), pp.517-547.10.1016/S0266-352X(01)00011-8Search in Google Scholar

Kim, K. D., Lee, S., Oh, H. J. 2009. Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environmental Geology, 58(1), pp. 61-70.Search in Google Scholar

Lee, S., Park, I., Choi, J. K. 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental management, 49(2), pp. 347-358.Search in Google Scholar

Neaupane, K.M., Adhikari, N.R., 2006. Prediction of tunneling-induced ground movementwith the multi-layer perceptron. Tunnelling and Underground Space Technology, 21, pp. 151–159.10.1016/j.tust.2005.07.001Search in Google Scholar

Santos Jr, O. J., Celestino, T. B., 2008. Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunnelling and underground space technology, 23(5), pp. 481-491.10.1016/j.tust.2007.07.002Search in Google Scholar

Sou-Sen, L., Hsien-Chuang, L., 2004. Neural-network-based regression model of ground surface settlement induced by deep excavation. Automation in construction, 13(3), pp. 279-289.10.1016/S0926-5805(03)00018-9Search in Google Scholar

Supplementary Report on Geotechnical Studies of Tabriz Subway 8 Stations and Tunnel Episode 9 6 of Line 1, National Construction - Engineers CompanySearch in Google Scholar

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), pp. 133-150.10.1016/j.tust.2005.06.007Search in Google Scholar

eISSN:
2284-7197
ISSN:
2247-3769
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Engineering, Introductions and Overviews, other, Electrical Engineering, Energy Engineering, Geosciences, Geodesy