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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| Published in: | Automation in construction Vol. 156; p. 105128 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2023
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| ISSN: | 0926-5805, 1872-7891 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 105128 |
| Author | Rust, Romana Bucher, Martin Juan José Tang, Siyu Kraus, Michael Anton |
| Author_xml | – sequence: 1 givenname: Martin Juan José orcidid: 0000-0002-5254-6131 surname: Bucher fullname: Bucher, Martin Juan José email: martin@mnbucher.com – sequence: 2 givenname: Michael Anton orcidid: 0000-0002-5000-2923 surname: Kraus fullname: Kraus, Michael Anton email: kraus@ibk.baug.ethz.ch – sequence: 3 givenname: Romana orcidid: 0000-0003-3722-8132 surname: Rust fullname: Rust, Romana email: rust@arch.ethz.ch – sequence: 4 givenname: Siyu orcidid: 0000-0002-1015-4770 surname: Tang fullname: Tang, Siyu email: siyu.tang@inf.ethz.ch |
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| Keywords | Performance-based design Variational autoencoder Deep generative design Generative design Deep generative modeling Artificial intelligence |
| Language | English |
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