Improved SelfSim for Inverse Extraction of Nonuniform, Nonlinear, and Inelastic Material Behavior under Cyclic Loadings

Abstract

AbstractIn this paper, an improved self-learning simulation (SelfSim) method is proposed for the inverse extraction of nonuniform, inelastic, and nonlinear material behavior under cyclic loadings. The SelfSim has been used to inversely extract local inelastic and nonlinear behavior of materials using limited global boundary responses. However, the SelfSim with conventional artificial neural network (ANN) models needs ad hoc data processing that frequently interrupts SelfSim training even in training monotonic constitutive behavior. To overcome this problem, an improved SelfSim with a new ANN-based hysteretic model is proposed. In addition, the ANN material model is implemented with considerations of large volume changes and geometric nonlinearity. The new SelfSim shows superior performances in the inverse modeling of complex material behavior under “multiaxial” and “cyclic” stress states. Two simulated numerical tests using a laminated rubber bearing with reinforcing steel shims are used to demonstrate th...