ABSTRACT
In this paper, a neural network is employed to predict the soil water characteristic curve of unsaturated soils. The network has five input neurons, namely, initial void ratio, initial gravimetric water content, logarithm of suction normalized with respect to the atmospheric pressure, clay fraction, and silt content. The network has five neurons in the hidden layer and the only output neuron is the gravimetric water content corresponding to the assigned input suction. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam compiled in SoilVision software, are used for training and testing of the network. After digitization of the data, a computer program coded in Matlab is developed and used for the analysis. The neural network simulations are compared with the experimental results as well as those of a number of models proposed in the literature. The results indicate the superior performance of the proposed method in predicting the soil water characteristic curve.
