Abstract
As a key engineering parameter, the shear strength of sand-clay mixtures needs to be determined before the design and construction of impervious liners for waste landfill sites are performed. The traditional method for determining the shear strength requires considerable time and substantial professional skills. This study focused on the estimation of the shear strength of sand-clay mixtures using the artificial neural network (ANN) and low-field nuclear magnetic resonance (NMR) spectroscopy. In this study, NMR tests and triaxial compression tests were carried out on 160 artificial sand-clay mixtures with different mineralogical compositions, water contents, and dry densities in the laboratory to obtain the T2 spectra and shear strength indices, respectively. Twelve characteristic variables that could reflect the pore structure and water classification in the mixtures were calculated for each T2 spectrum. A novel predictive model for the shear strength of the mixtures was established using the ANN based on 12 characteristic variables, the Atterberg limits, and the tested shear strengths of mixtures. The Atterberg limits of the mixtures, 12 characteristic variables and shear strengths of the mixtures were defined as the input factors, input covariates and response variables, respectively. The model was proven to have a sufficiently high prediction capability by using the Pearson correlation coefficient (R), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE).