Simulation of torsional shear test results with neuro-fuzzy control system

Abstract

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1 Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear stress-strain changes, the number of cycles to reach liquefaction and pore pressure variations were recorded. Results indicate that the liquefaction resistances of uniform sands are significantly affected by the method of sample preparation and initial conditions. In the second phase, an adaptive neuro-fuzzy inference methodology was applied for the simulation of nonlinear mapping among radial stress conditions, relative density, and number of load repetitions necessary for the liquefaction. The neuro-fuzzy model included a fuzzy approximate reasoning through a Sugeno fuzzy inference system (FIS) and the input space was fuzzified by a grid-partitioning technique. A hybrid-learning algorithm was selected for the adaptation of model parameters. Furthermore, linear-nonlinear regression analyses and neural network models were employed to observe relative performances. It is concluded based on the findings that the neuro-fuzzy control system exhibited a superior performance as compared to other employed methods. (C) 2008 Elsevier Ltd. All rights reserved.