## 5 Conclusions  
In this work, we have assessed the possibly nested relationship between architectural form and structural response of tall buildings with a diagrid structure to external loading. To envisage an automatic strategy to get insights into which structural and geometrical parameters may improve the structural response, different ML algorithms have been exploited. As a first step in our investigation, the performances of a set of well-known ML classifiers have been compared by referring to a label related to a conventional seismic load, namely the drift measured at the top of the building. The classification accuracy and the training time of the different algorithms have been compared. The decision tree and the ensemble method algorithms have shown the most promising results, especially in terms of a trade off between accuracy and computational burden. The role of algorithmic hyperparameters has been also discussed for each classifier; in all cases, the use of an optimizer has been reported to increase the model accuracy. Principal component analysis, adopted to reduce the complexity of the model, has resulted to be not useful: although rather good results have been almost always attained, the benefits offered by PCA in terms of speeding up the training have been compensated for by a decreased classification accuracy, which is here adopted to judge the performance of each single algorithm to work on a small set of building geometries. The role of the selected features, like the top/bottom plan geometry, the diagrid degree, the total mass of the structure, the drift induced by the seismic load, the expected design weight, the maximum utilization, the total length of diagrid, and the maximum normal force, is not obviously neutral: in most of the cases, by excluding a few of the aforementioned features, the accuracy was affected. In view of the present results, it can be claimed that machine learning tools can be efficiently adopted to the aim here envisaged, leading to noteworthy advantages and support during the early stage of the architectural design. At difference from the naive approach mentioned in Section 2, based on the observation by the naked eye of diagrams such as Figure 3, the ML-based classification permits to deal with a far larger number of architectural shapes (e.g., thousands of them) in a fast and clear way, restricting the results to a quantitative well-defined feature and finding the hidden patterns. Even if classification is not a predictive tool, still this approach allows to identify (with an error lower than 5%) the drift range for a given top-bottom geometry combination: an encouraging result for further applications in this field. In future activities we are going to move towards the use of neural networks, like, e.g., (variational) autoencoders, to provide a generative approach that could suggest new groundbreaking architectural solutions, starting from the best ones identified as promising by the present classification approach. This methodology would provide the way, if properly linked to parametric design tools, to further enlarge the dataset of building geometries by including more complex solutions, not necessarily generated by a morph of the plan from top to bottom, as here considered. In order to also overcome the issues related to the small dimension of the dataset, other generative approaches, like, e.g., generative adversarial networks, could be adopted. Another research line to be addressed points to a regression analysis instead of a classification one, so that predictions linked to a continuous structural performance indicator will be provided instead of discrete classes. Overall, unsupervised learning methods will be looked for to categorize all the observations, and provide the best automation of the entire procedure. Conceptualization and methodology, P.K., A.G. and S.M. ; software, P.K. ; validation, P.K., A.G. and S.M. ; data curation, P.K. ; writing--original draft preparation, P.K. and A.G.; writing--review and editing, A.G. and S.M. ; supervision, A.G. and S.M.