Simulating the stressstrain behavior of Georgia kaolin via recurrent neuronet approach
Many geotechnical engineering problems, such as slope stability analysis, require understanding of the constitutive behavior of clay under plane strain loading condition. Although studies on the mechanism of clay behavior had been carried out for many years, current theoretical accomplishments still cannot provide reliable simulations for clay under plane strain loading encountered in engineering practice. Traditional constitutive modeling, which involves building mathematical formulations with parameters determined from clay tests, seems to be an attractive approach. However, the complexity of the formulation, the large number of parameters involved, and the difficulty and time required to conduct clay tests make this approach impractical. As a result, there are no available reliable simulation models that can efficiently characterize the clay response under plane strain loading. Lack of appropriate simulation (constitutive) model for clay under plane strain loading greatly hinders the analysis, design, and construction with this material in engineering practice. Therefore, it is worthy to find another way to study the clay response under plane strain loading. In this paper, a recurrent neuronet-based model was developed in order to simulate clay behavior under plane strain loading conditions. This model was then used to investigate the effects of loading rate and stress history on clay response. The predicted responses showed that the developed neuronet-based model was successfully able to qualitatively assess the impact of strain rate and stress history on clay behavior. Accordingly, it is found that clay behavior is more sensitive to its consolidation stress history than to its strain loading rate.
stressstrain artificial neural networks neuro-engineering clay modeling