Yl. Mo et Rh. Han, INVESTIGATION OF PRESTRESSED CONCRETE FRAME BEHAVIOR WITH NEURAL NETWORKS, Journal of intelligent material systems and structures, 6(4), 1995, pp. 566-573
To date, concrete structure modeling has involved the development of m
athematical models of concrete structure behavior derived from human o
bservation of, and reasoning with, experimental data. An alternative,
discussed in this paper, is to use a computation and knowledge represe
ntation paradigm, called neural networks, developed by researchers in
connectionism (a subfield of artificial intelligence) to model concret
e structure behavior. The main benefits in using a neural-network appr
oach are that all behavior can be represented within a unified environ
ment of a neural network and that the network is built directly from e
xperimental 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 modelin
g strategy has important implications for modeling the behavior of mod
ern, complex concrete structures. In this paper, the behavior of prest
ressed concrete frames is modeled with a back-propagation neural netwo
rk. The preliminary results of using networks to model concrete struct
ure look very promising.