To date, concrete structure modelling has involved the development of
mathematical models of concrete structure behaviour derived from human
observation of, and reasoning with, experimental data. An alternative
, discussed in this Paper, is to use a computation and knowledge repre
sentation paradigm, called neural networks, developed by researchers i
n connectionism (a sub-field of artificial intelligence) to model conc
rete structure behaviour. The main benefits in using a neural network
approach are that all behaviour can be represented within a unified en
vironment of a neural network and the network is built directly from o
m experimental data using the self-organizing capabilities of the neur
al network, i.e. the network is presented with the experimental data a
nd learns the relationships between stresses and strains. Such a strat
egy has important implications for modelling the behaviour of modern,
complex concrete structures. In this Paper, the behaviour of reinforce
d concrete framed shearwalls is modelled with a back-propagation neura
l network. Preliminary results of the use of networks to model concret
e structure look promising.