###### Abstract  
We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is mapped onto their skin. A supervised machine learning approach is then adopted to learn the features of the aforementioned relationship. Six different classifiers, namely k-nearest neighbour, support vector machine, decision tree, ensemble method, discriminant analysis, and naive Bayes, are adopted to this aim, targeting the structural response as the building drift, i.e., the lateral displacement at its top under the considered external excitation. By focusing on the classification of the structural response, it is shown that some classifiers, like, e.g., decision tree, k-nearest neighbour and the ensemble method, can learn well the structural behavior, and can therefore help design teams to select more efficient structural solutions. supervised machine learning classification; tall buildings; architectural form generation; structurally informed design; parametric design +
Footnote †: journal: _Algorithms_  
fusing them with model-based predictions, efficient strategies for structural health monitoring have been extensively investigated [3; 4]. Less frequent is instead the investigation of data-driven approaches to design, for example, in terms of construction geometries for specific purposes [5]. In this context, parametric design [6] proved to be a powerful tool to scrutinize alternative solutions within an architectural form-structural efficiency conundrum [7]. In this work, we consider a building category for which both structural performance and architectural form play a relevant role: tall buildings with outer diagrids [8; 9]. In such cases, the structure strongly influences the architecture, because a significant vertical support and resistance to lateral loads are both needed [10]. We investigate here the use of supervised ML techniques to discriminate between different solutions, as tall buildings are structures requiring not only a considerable effort in terms of construction time and money, but also in terms of footprint related to materials and energy necessary during their entire life-cycle, an issue that is nowadays more and more a matter of concern [11]. Furthermore, the construction of tall buildings has become a collaborative effort: competences and expertise of different people, whose interests must be composed, are all considered. A number of decisions taken at the beginning of the design phase are known to affect the overall result, often leading to sub-optimal solutions at later project stages [6]: it has been estimated that 80% of material consumption in the construction is set by this early stage of the project, and construction costs can rise up to one-third of the total budget [9; 12; 13]. However, in several cases, structural issues are not adequately taken into account, and the architectural form drives the design [14]. Paradoxically, in [6], it was declared that in the past there was a more efficient collaboration between architects and structural engineers during the design of tall buildings: the [PERSON] tower [15] is one of the outstanding examples in this regard. With the development of construction technology, architects and engineers have looked for more complex forms and structural solutions, and the structural response is sometimes detrimentally affected. Regarding the exploration of the relationship between the architectural form and the structural behavior of tall buildings with outer diagrids, in [16] it was reported that the hyperboloid form is more efficient than the cylindrical one. Other investigations of the aforementioned relationship have considered geometries like e.g., shell and lattice towers, see [17; 18; 19]. Moreover, in [20] it was concluded that the tapering of tall buildings has a more significant effect under seismic loadings than changes to floor plans.