## 2 Workflow  
The proposed procedure consists of three main steps:  
1. Form generation through a parametric design tool;
2. Structural analysis allowing for building weight, dead load and seismic load;
3. Classification learning, to allow discriminating the building factors having an impact on the structural response under the considered loading conditions. The focus in this work is on a proposal regarding the last step, while the former two are briefly summarized in this Section; additional details can be found in [30; 31]. A prerequisite behind the application of any ML approach is known to be represented by data availability: the ML algorithm then has to exploit (a subset of) the handled dataset to learn the link between the features of the structures on one side, and the response under pre-defined loadings (even in terms of environmental or operational variability) on the other side. The research goal of learning is to judge whether any geometry (typically, those unseen during learning) can be accurately classified. In Section 3, it will be shown that this classification can be extremely difficult to carry out, even with a kind of artificial intelligence to govern the procedure, due to the large number of features allowed for in the analysis and to the complexity in their interplay. We should emphasize that a classification approach is able to deal with existing data, and not to foresee new ones for other geometries: we will return to this issue, in particular regarding generative approaches, in the discussion related to future activities in Section 5. Under the aforementioned limitations, the main objective of the present work is to find a suitable algorithm to classify the response of tall buildings with an outer diagrid structure under the considered actions. _Architectural and Structural Modelling_  
By exploiting parametric design, the architectural form of a tall building is dealt with as a three-dimensional shape characterised by: the geometry of top and bottom plans; the vertical transformation between the aforementioned geometries, namely the morph or twist or curvilinear transformation, occurring along the building elevation, see [32]. At each story, the geometry is therefore a polygon; in our investigation the shapes at the top and at the bottom are chosen to be a 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 24 sided polygon. Hence, there are 12 alternative choices for the bottom plan and other 12 choices for the top plan, leading to a total of 144 building forms. The top and bottom plan areas are supposed to be 500 and 2025 square meters, respectively; the tall buildings are vertically tapered to guarantee better lateral stability with respect to constant-section buildings [33]. The total height is set in such a way that the total usable area of all buildings is maintained at 70,000 square meters \(\pm\) 1%: therefore, the number of floors may change in order to comply with this constraint. These settings are extracted from another research [32] and modified by architectural consideration of actual tall buildings, including parameters like lease span (i.e., distance from the core to the exterior window) and the minimum core dimension. The total elevation of each model can be calculated by multiplying the distance from floor to floor (4 m) by the number of stories, resulting in a total height of 232 m (58 stories) or 236 m (59 stories). The aspect ratio, i.e., the ratio of height to the largest horizontal dimension of the bottom plan, ranges from 2.93 to 4.62. The maximum in-plane dimension of the bottom plan ranges between 45 and 59 m, while the average is about 51 m. In Figure 1, the entire set of architectural forms is depicted in perspective and in-plan views, to provide an idea regarding how the 144 models differ from each other.