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- W2013314780 abstract "In 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." @default.
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- W2013314780 date "2013-10-01" @default.
- W2013314780 modified "2023-09-23" @default.
- W2013314780 title "Artificial Neural Network Analysis of Twin Tunnelling-Induced Ground Settlements" @default.
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- W2013314780 doi "https://doi.org/10.1109/smc.2013.425" @default.
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