Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system

Neuro-fuzzy inference systems have been used in many areas in civil engineering applications. This study was conducted to estimate low strain dynamic properties of composite media from easily measurable physical properties using the adaptive neuro-fuzzy inference system (ANFIS). The inference system was employed to predict the shear modulus and the damping coefficient of the sand samples as an alternative to lengthy laboratory testing. ANFIS was trained using low strain dynamic test results of samples of sand reinforced with particulate rubber inclusions from a resonant column device. The training was performed with an improved hybrid method, which was found to deliver better results than classical back-propagation method such as multi-layer perceptron (MLP) and multiple regression analysis method (MRM). Using the new approach, the optimal precise value of a parameter could be estimated within the constraints of the experimental design. The ANFIS model has appeared very effective in modeling complex soil properties such as shear modulus and damping coefficient, and performs better than MLP and MRM.