In this paper the use of a nontraditional technique, neural networks, has b
een investigated as a means to develop efficient predictive models of the s
tructural behavior of concrete slabs. The applicability of neural networks
within the realm of structural analysis is first reviewed, along with their
state-of-the-art applications and research efforts. Four neural networks h
ave then been developed to model the load-deflection behavior of concrete s
labs, the final crack-pattern formation, and both the reinforcing-steel and
concrete strain distributions at failure. The four neural networks were tr
ained and tested using the experimental results of 38 full-scale slabs. Det
ails regarding data modeling, neural network training, and performance eval
uation are described. Using the developed networks, a spreadsheet tool for
the structural analysis of concrete slabs was developed with a simple inter
face, automated predictions, and what-if capabilities. The developed tool i
s useful for teaching purposes and for reasonable prediction of the behavio
r of concrete slabs without additional experimental testing.