Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation

Abstract

Artificial intelligence (AI) and machine learning (ML) techniques are transforming building engineering. This work goes through the critical role of architectural parameters in influencing the structural responses of tall buildings, with a special focus on diagrid structures. The main aim of this study is to demonstrate how ML can improve the early design phase of diagrid buildings. Using a small, initially collected data set, enhanced through data augmentation, the classification of diagrid buildings in terms of design feasibility is investigated. This study identifies key architectural and structural parameters, employing various filter and wrapper methods for feature selection. The results show that our methods are effective in producing high-quality synthetic data, maintaining stable learning accuracies, and establishing accurate and robust relationships between architectural parameters and structural responses in diagrid buildings. These insights are crucial for facilitating more effective design processes in the realm of high-rise diagrid building design.