The use of equivalent beam models to estimate the dynamical characteristics of complex tall buildings has been investigated by several authors. The main reason is the structural response estimation to stochastic loads, such as wind and earthquake, using a reduced number of degrees of freedom, which reduces the computational costs and therefore gives the designer an effective tool to explore a number of possible structural solutions. In this paper, a novel approach to calibrate the mechanical and dynamical features of a complete 3D Timoshenko beam, i.e., describing bending, shear and torsional behavior, is proposed.

In this paper, we describe and present the results of meme detection system, specifically developed and submitted for our participation to the first subtask of DANKMEMES (EVALITA 2020). We built simple classifiers, consisting in feed forward neural networks. They leverage existing pretrained embeddings, both for text and image representation. Our best system (SNK1) achieves good results in meme detection (F1 = 0.8473), ranking 2nd in the competition, at a distance of 0.0028 from the first classified.