Typically material modeling has involved the development of mathematic
al models of material behavior derived from human observation of exper
imental data. An alternative procedure, discussed in this paper, is to
use a computation and knowledge representation paradigm, called a neu
ral network, to model material behavior. The main benefits in using a
neural network approach are that all behavior can be represented withi
n the unified environment of a neural network and that the network is
built directly from experimental data using the self-organizing capabi
lities of the neural network, meaning that the network is presented wi
th 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 mechani
cal behavior of rebars affected by welds is modeled with a back-propag
ation neural network. The results of using networks to study the effec
t of welds on rebars look very promising.