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Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models
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DOI：10.1016/j.autcon.2023.105128.png)
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Abstract
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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.
Keywords:
Deep generative modeling
Performance-based design
Generative design
Variational autoencoder
Deep generative design
Artificial intelligence
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