Learning the link between architectural form and structural efficiency: a supervised machine learning approach

In this work, we exploit supervised machine learning (ML) to investigate the relationship between architectural form and structural efficiency under seismic excitations. We inspect a small dataset of simulated responses of tall buildings, differing in terms of base and top plans within which a vertical transformation method is adopted (tapered forms). A diagrid structure with members having a tubular cross-section is mapped on the architectural forms, and static loads equivalent to the seismic excitation are applied. Different ML algorithms, such as KNN, SVM, Decision Tree, Ensemble, Discriminant, Naïve Bayes are next trained, to classify the seismic response of each form on the basis of a specific label. Results to be presented rely upon the drift of the building at its top floor, though the same procedure can be generalized and adopt any performance characteristic of the considered structure, like e.g. the drift ratio, total mass, or expected design weight. The classification algorithms are all tested within a Bayesian optimization approach; it is then found that the Decision Tree classifier provides the highest accuracy, linked to the lowest computing time. This research activity put forward a promising perspective for the use of ML algorithms to help architectural and structural designers during the early stages of conception and control of tall buildings.