Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models - Bucher, Martin Juan José; Kraus, Michael Anton; Rust, Romana; Tang, Siyu (Automation in construction, 2023)
Parametric Modeling, Generative Design, and Performance-Based Design have gained increasing attention in the AEC field as a way to create a wide range of design variants while focusing on performance attributes rather than building codes. However, the relationships between design parameters and performance attributes are often very complex, resulting in a highly iterative and unguided process. In this paper, we argue that a more goal-oriented design process is enabled by an inverse formulation that starts with performance attributes instead of design parameters. A Deep Conditional Generative Design workflow is proposed that takes a set of performance attributes and partially defined design features as input and produces a complete set of design parameters as output. A model architecture based on a Conditional Variational Autoencoder is presented along with different approximate posteriors, and evaluated on four different case studies. Compared to Genetic Algorithms, our method proves superior when utilizing a pre-trained model. •Complex 2D/3D models hinder understanding of relationships in Generative Design.•Deep Conditional Generative Design learns joint distribution for targeted designs.•Inverse formulation enables precise control for Performance-Based Generative Design.•Our Conditional Variational Autoencoder is evaluated against Genetic Algorithms.•Expressive posterior improves performance; partial conditioning shows promise.