Modelling damping ratio and shear modulus of sand–mica mixtures using neural networks

This study proposes two neural network (NN) models for damping ratio and shear modulus of sand–mica mixtures based on experimental results. The experimental database used for NN modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe Resonant Column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R2 = 0.97 for damping ratio and R2 = 0.99 for shear modulus). Moreover the proposed NN models are also presented as simple explicit mathematical functions for further use by researchers.