Artificial neural networks for structural analysis

In this work we use the continuous Hopfield network and the continuous bidirectional associative memory system (BAM) in order to develop two novel methods for structural analysis. The development of these techniques is based on the analogous relationship that results from comparing the energy functions of the above two models with that of the structural displacement method (i.e. the so-called stiffness matrix method) and it takes advantage of the fact that classical numerical methods do not have the characteristics of parallel computation that artificial neural networks have. Several examples related to structural deformation are used to illustrate the superiority of the BAM-based neural networks over other traditional numerical methods and the Hopfield model, especially for the case of large dimensional stiffness matrices.