Probabilistic neural networks for seismic damage mechanisms prediction

Abstract

The procedure commonly employed to assess the seismic vulnerability of buildings uses simplified qualitative and quantitative observations obtained from the measured data entered into report forms. In Italy, the data sheets adopted by the National Defence Group against Earthquakes (Gruppo Nazionale per la Difesa dai Terremoti-GNDT) play a unifying and reference role. This paper proposes a method for the processing of the data contained in such report forms which is based on probabilistic neural networks producing a Bayesian classification. The final goal is to exploit the fundamental learning and generalization capabilities of neural networks to obtain an estimate of the vulnerability of structural systems. In particular, the aim is to be able to predict the damage mechanisms which may be triggered in the macro-elements of public worship buildings.