A proposal of an alternative method for estimating the hydraulic conductivity of unsaturated soils

The determination of hydraulic conductivity in unsaturated soils is essential when performing transient flow analysis in these porous media. However, the execution of laboratory and field tests to determine this hydraulic property is not a current practice in the scope of geotechnics, as these are time-consuming and expensive procedures. Artificial neural networks (ANN) have been widely used in Soil Mechanics, allowing the estimation of complex and multivariate phenomena in an easy and simple way. Thus, this article aims to present a model for estimating hydraulic conductivity in unsaturated soils developed from a type of ANN known as multilayer perceptron (MLP). The model's input variables are the initial void ratio, initial gravimetric water content, sand content, silt content, clay content, plasticity index, saturated permeability coefficient and matric suction. During modeling, a total of 275 examples were used, of which 85% were used in the training phase, and 15% in the testing phase. The proposed model has an A: 8-4-2-1 architecture and presented a correlation coefficient of 0.97 after 500 thousand iterations in both training and testing phases. The results of the model adjusted satisfactorily to the experimental data used in the training and test phases, and the proposed neural network was able to represent the influence of the input variables on the hydraulic behavior of different types of soil.