ISBN:  Unavailable

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Page count: 10

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.