Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models

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 perf...

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Bibliographic Details
Published in:Automation in construction Vol. 156; p. 105128
Main Authors: Bucher, Martin Juan José, Kraus, Michael Anton, Rust, Romana, Tang, Siyu
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2023
Subjects:
Artificial intelligence
Deep generative design
Deep generative modeling
Generative design
Performance-based design
Variational autoencoder
Performance-based design
Variational autoencoder
Deep generative design
Generative design
Deep generative modeling
Artificial intelligence
ISSN:0926-5805, 1872-7891
Online Access:Get full text
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Summary: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.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2023.105128