Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks

This paper presents the application using a multilayer neural network to model nonlinear elastic behavior of composite soil reinforced with fiber and stabilized with lime. First, shear modulus of the reinforced soil was assumed to be a nonlinear function of multiple variables such as contents of short fiber and lime powder, confining pressure, sample-aging period as well as shear strain. Secondly, a multilayer neural network was designed to map the highly nonlinear relationship between shear stress and strain. Thirdly, conventional triaxial shearing tests have been conducted for 34 sets of soil samples to provide experimental data for training and validating the neural network model. Finally, the neural network-based parameter sensitivities have been analyzed. The results of sensitivity analysis indicate that the lime content and the sample curing time play more significant roles than the fiber content in improving soil mechanical properties. It is the first attempt to apply the neural network to modeling of elastic behavior of composite soils, and has been found that modeling of reinforced soil using a multilayer neural network can provide more quality information on the performance of reinforced soil for better decision-making and continuous improvement of construction material designs.