The application of artificial neural networks to capture structural de
sign expertise is demonstrated. The principal advantage of a trained n
eural network is that it requires a trivial computational effort to pr
oduce an acceptable new design. For the class of problems addressed, t
he development of a conventional expert system would be extremely diff
icult. In the present effort, a structural optimization code with mult
iple nonlinear programming algorithms and an artificial neural network
code NETS were used. A set of optimum designs for a ring and two airc
raft wings for static and dynamic constraints were generated using the
optimization codes. The optimum design data were processed to obtain
input and output pairs, which were used to develop a trained artificia
l neural network using the code NETS. Optimum designs for new design c
onditions were predicted using the trained network. Neural net predict
ion of optimum designs was found to be satisfactory for the majority o
f the output design parameters. However, results from the present stud
y indicate that caution must be exercised to ensure that all design va
riables are within selected error-bounds.