###### Abstract
A similar study [21] examined the effects of top and bottom plan geometries and diagrid angles, and optimal diagrid angles between 53\({}^{\circ}\) and 70\({}^{\circ}\) together with a larger number of sides showed the best results. Even though ML was exploited in several areas of the scientific disciplines, in this multidisciplinary area where architecture and structural design co-exist, it does not appear to be fully investigated. To focus on the most striking examples in the literature, we mention that in [22], ML was applied to provide non-conventional structural shapes that were categorized according to the final design solution, getting insight into the relevant structural efficiency. Artificial intelligence led to more creative design processes in [23], by adopting a variational autoencoder to generate different architectural samples. In [24], generative adversarial networks were adopted to carry out a cost-benefit study and motivate retro-cladding in residential buildings: a large set of synthetic images was created by exploiting computer aided design renderings, to be later adopted by ML algorithms. In [25], ML was adopted in the early design phase to study the structural performance of a shell structure modelled parametrically: the dataset included nine structural parameters, combined with 42 material parameters, and a nonlinear regression and artificial neural networks were used within a supervised frame. In the context of non-structural applications of ML, in [26] information was extracted from 15 architectural building information models, and rated according to two subjective labels, livability, and sleep comfort. In this work, by moving some steps in a largely unexplored research area, we exploit parametric design tools to feed a ML-based approach and investigate the response to conventional seismic actions of alternative structural and architectural solutions. By means of a parametric design approach [6], features like the architectural form can be in principle optimized to match site-specific requirements [27]. The entire design process can thus become extremely flexible, able to self-adapt under changing requirements, due to, e.g., client needs and other external effects [27]. Traditional approaches to link to architectural form and structural efficiency are based on the analysis of a large number of tall building models, resulting to be extremely time-consuming. An alternative approach based on genetic algorithms to optimize the structural efficiency of the said tall buildings would allow us to handle fewer geometrical models; conversely, it turns out to be longer to get some optimal configurations [21; 28; 29]. By means of a ML-based approach, a similarly limited dataset of building geometries can be instead adopted to learn the relationship between architectural parameters (such as the side count of the top and bottom floor plans) and the structural response to some specific actions, like those related to possible earthquakes. The structural response of new models can be predicted on the basis of the learned features or relationship. In other words, this approach is in principle capable of tackling all obstacles to classify the response of tall buildings, rapidly and accurately. To describe the ML-based procedure in detail and validate it, the remainder of the paper is organized as follows. In Section 2, digital models of tall buildings with a specific geometry are described, as obtained via parametric design tools and characterized by outer diagrid structures; these models are supposed to be excited by lateral loads related to a conventional seismic action, fully described in the following. Various learning algorithms are described in Section 3; they are adopted in a supervised ML approach for feature classification, according to the structural response of the models. Results in Section 4 provide a rationale behind the optimal classification algorithms, showing how labels and selected structural performance indices have a role in the entire process.