Predicting the dynamic properties of glyben using a modular neural network (MNN)

Glyben is an artificial soil comprised of bentonite and glycerin that is used in scaled physical model testing. The dynamic properties of glyben are strongly influenced by the percent glycerin by mass in the artificial soil, temperature, time after mixing, shear strain amplitude, excitation frequency, and confining stress. This paper describes the development and testing of a modular neural network (MNN) that is suitable for predicting the dynamic properties of glyben. The MNN architecture comprises an input layer, two expert modules (neural networks) linked by a gating network, and an output layer. The MNN is trained using 124 datasets obtained from the literature and tested as part of the current study to evaluate its accuracy. It is shown that the developed MNN can adequately predict the dynamic properties of glyben.