Investigation of Stress-Strain Relationship of Confined Concrete in Hollow Bridge Columns Using Neural Networks

Abstract

Abstract Typically, material modeling has involved the development of mathematical models of material behavior derived from human observation of experimental data. An alternative procedure, discussed in this paper, is to use a computation and knowledge representation paradigm, called a network, to model material behavior. The main benefits in using a neural network is that the network is built directly from experimental data using the self-organizing capabilities of the neural network, i.e., the network is presented with the experimental data and learns the relationships between stresses and strains. Such a modeling strategy has important implications for modeling the behavior of complex materials. In this paper, the stress-strain relationship of confined concrete in hollow bridge columns is modeled with a back-propagation neural network. The results of using networks to study the behavior of confined concrete look very promising.