The shear modulus and normalized shear modulus degradation curve are the fundamental parameters describing soil behavior. Thus, this article is focused on the stiffness characteristic of 15 different Warsaw cohesive soli represented by the parameters mentioned above. In this research, standard resonant column tests were performed in a wide shear strain range, from a small one, where soil behaves like an elastic medium, to a medium one, where soil has an unrecoverable deformation. Collected data allows the authors to create empirical models describing stiffness characteristics with high reliability. The maximum shear modulus calculated by the proposed equation for Warsaw cohesive soil had a relative error of about 6.8%. The formula for normalized shear modulus estimated G/G_MAX with 2.2% relative error. Combined empirical models for G_MAX, and G/G_MAX allow the evaluation of Warsaw cohesive soil’s shear modulus value in a wide shear deformation range, with a very low value of the relative error of 6.7%.

The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature. The ANN model was developed using a series of laboratory tests of the damping ratio performed in the Resonance Column. Predicted values of the damping ratio of clay soils obtained from the ANN model are characterized by high convergence (coefficient of determination R² = 0.976). In comparison with other published empirical formulas, the ANN model showed an improvement in the prediction accuracy. What is more, ANN models proved to be more flexible compared to formulas and relationships with a predetermined structure, and they were well suited to modeling the complex behavior of most geotechnical engineering materials, which, by their very nature, exhibit extreme variability. In conclusion, ANNs have the potential to predict the damping ratio (D) of clay soils and can do much better than traditional statistical techniques.