| A neural network-based material modeling methodology for engineering materials is developed in this study. With this material modeling methodology, the stress-strain behavior of a material is captured within the distributed weight structure of a multilayer feedforward neural network trained directly on the stress-strain data obtained from experiments. The feasibility of this approach is verified through constructing neural network-based constitutive models of plain concrete in biaxial stress states and in uniaxial cyclic compression. A composite material model simulating the stress-strain behavior of reinforced concrete as a generic composite material in a biaxial stress state is built with experimental data from Vecchio and Collins' tests on reinforced concrete panels in both pure shear and combined shear with normal stresses.; An adaptive neural network simulator is developed by implementing a dynamic node creation scheme and a higher order learning algorithm. Representation schemes, network architectures, training and testing methods, stress- and strain-based approaches for material modeling are investigated. An elastic unloading mechanism is studied with a concrete material model in biaxial compression. Main issues concerning the implementation of neural network material models in finite element solution procedures are briefly discussed. The results on the stress-strain relations of a material predicted by a neural network-based model are compared with experimental data. The developed approach shows promise in the constitutive modeling of composite materials. |
