Abstract
The study of unsaturated soil is essential for engineers who construct dams, tunnels, water conveyance channels, mines, and other structures. Groundwater must also be taken into account when devising measures to control ground settlement or subsidence caused by dewatering. An artificial neural network (ANN) is a mathematical model or computational model that is inspired by the structure or functional aspects of biological neural networks. In this study the authors used ANN as a non-linear statistical data modelling tool for assessing the 3-D model of soil’s unsaturated depth. Based on the obtained results, it can be stated that the trained neural network is capable in 3-D modelling of soil’s unsaturated depth with an acceptable level of confidence and it should be added that the mentioned ANN is useful to model complex relationships between input and outputs or to find patterns in data for prediction of ground water table in study area.
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Choobbasti, A.J., Shooshpasha, E., Farrokhzad, F. (2012). 3-D Modeling of Soil’s Unsaturated Depth Using Artificial Neural Network (Case Study of Babol). In: Mancuso, C., Jommi, C., D’Onza, F. (eds) Unsaturated Soils: Research and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31343-1_40
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DOI: https://doi.org/10.1007/978-3-642-31343-1_40
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