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Artificial neural network analysis of twin tunnelling-induced ground settlements
conference contribution
posted on 2013-01-01, 00:00 authored by S Khatami, A Mirhabibi, Abbas KhosraviAbbas Khosravi, Saeid NahavandiIn this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.
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Event
IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)Pagination
2492 - 2498Publisher
IEEELocation
Manchester, EnglandPlace of publication
Piscataway, N.J.Start date
2013-10-13End date
2013-10-16Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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