Seismic vulnerability assessment of chemical plants through probabilistic neural networks

A chemical industrial plant represents a sensitive presence in a region and, in case of severe damage due to earthquake actions, its impact on social life and environment can be devastating. From the structural point of view, chemical plants count a number of recurrent elements, which are classifiable in a discrete set of typological families (towers, chimneys, cylindrical or spherical or prismatic tanks, pipes etc.). The final aim of this work is to outline a general procedure to be followed in order to assign a seismic vulnerability estimate to each element of the various typological families. s paper, F.E. simulations allowed to create a training set, which has been used to train a probabilistic neural system. A sample application has concerned the seismic vulnerability of simple spherical tanks.