Neural network based prediction schemes of the non-linear seismic response of 3D buildings

Since early 1980s new families of computational methods, termed as soft computing (SC) methods, have been proposed. SC methods are based on heuristic approaches rather than on rigorous mathematics while their use in various areas of computational mechanics is continuously growing. Artificial neural networks (ANNs), which have been applied in many engineering fields, are among the most popular SC methods. Computational earthquake engineering is a computationally intensive field where ANNs have been used for the simulation of the structural behaviour under static or dynamic loading. Performance-based design (PBD) is the current trend for the seismic design of structural systems where the structural performance is assessed for multiple hazard levels, requiring significant computational effort. In this work a new adaptive scheme is proposed in order to predict the structural non-linear behaviour when earthquake actions of increased severity are considered. The predicted structural response by ANNs can be used in the PBD framework when dynamic analyses are performed, aiming at reducing the excessive computational cost.