Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-Model-Based Conceptual Design of Pedestrian Bridges
Today, engineers rely on conventional iterative (often manual) techniques for conceptual design. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive, leaving room for improvement. This paper...
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| Vydané v: | Automation in construction Ročník 163; s. 105411 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.07.2024
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| ISSN: | 0926-5805, 1872-7891 |
| On-line prístup: | Získať plný text |
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| Abstract | Today, engineers rely on conventional iterative (often manual) techniques for conceptual design. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive, leaving room for improvement. This paper provides a design exploration and explanation framework to augment the designer via a Conditional Variational Autoencoder (CVAE), which serves as a forward performance predictor as well as an inverse design generator conditioned on a set of performance requests. Hence, the CVAE overcomes the limitations of traditional iterative techniques by learning a differentiable mapping for a highly nonlinear design space, thus enabling sensitivity analysis. These methods allow for informing designers about (i) relations of the model between features and performances and (ii) structural improvements under user-defined objectives. The framework is tested on a case-study and proves its potential to serve as a future co-pilot for conceptual design studies of diverse civil structures and beyond.
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•We propose a new generative design method employing (explainable) deep learning.•A novel autoencoder architecture for forward and inverse design is suggested.•Our novel method acts as a design co-pilot for enabling informed decision making.•A synthetic data pipeline for training the design co-pilot is developed.•Our method provides local sensitivity analysis with negligible computational overhead. |
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| AbstractList | Today, engineers rely on conventional iterative (often manual) techniques for conceptual design. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive, leaving room for improvement. This paper provides a design exploration and explanation framework to augment the designer via a Conditional Variational Autoencoder (CVAE), which serves as a forward performance predictor as well as an inverse design generator conditioned on a set of performance requests. Hence, the CVAE overcomes the limitations of traditional iterative techniques by learning a differentiable mapping for a highly nonlinear design space, thus enabling sensitivity analysis. These methods allow for informing designers about (i) relations of the model between features and performances and (ii) structural improvements under user-defined objectives. The framework is tested on a case-study and proves its potential to serve as a future co-pilot for conceptual design studies of diverse civil structures and beyond.
[Display omitted]
•We propose a new generative design method employing (explainable) deep learning.•A novel autoencoder architecture for forward and inverse design is suggested.•Our novel method acts as a design co-pilot for enabling informed decision making.•A synthetic data pipeline for training the design co-pilot is developed.•Our method provides local sensitivity analysis with negligible computational overhead. |
| ArticleNumber | 105411 |
| Author | Salamanca, Luis Bischof, Rafael Perez-Cruz, Fernando Kuhn, Sophia V. Kaufmann, Walter Kraus, Michael A. Balmer, Vera |
| Author_xml | – sequence: 1 givenname: Vera orcidid: 0000-0001-8353-0450 surname: Balmer fullname: Balmer, Vera email: vera.balmer@ibk.baug.ethz.ch organization: Institute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, Zurich, 8093, Switzerland – sequence: 2 givenname: Sophia V. orcidid: 0000-0002-0426-8675 surname: Kuhn fullname: Kuhn, Sophia V. email: sophia.kuhn@ibk.baug.ethz.ch organization: Institute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, Zurich, 8093, Switzerland – sequence: 3 givenname: Rafael orcidid: 0000-0002-6617-2767 surname: Bischof fullname: Bischof, Rafael organization: Center for Augmented Computational Design in Architecture, Engineering and Construction, ETH Zurich, Wolfgang-Pauli-Strasse 27, Zurich, 8093, Switzerland – sequence: 4 givenname: Luis orcidid: 0000-0001-9314-8466 surname: Salamanca fullname: Salamanca, Luis organization: Center for Augmented Computational Design in Architecture, Engineering and Construction, ETH Zurich, Wolfgang-Pauli-Strasse 27, Zurich, 8093, Switzerland – sequence: 5 givenname: Walter orcidid: 0000-0002-8415-4896 surname: Kaufmann fullname: Kaufmann, Walter organization: Institute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, Zurich, 8093, Switzerland – sequence: 6 givenname: Fernando orcidid: 0000-0001-8996-5076 surname: Perez-Cruz fullname: Perez-Cruz, Fernando organization: Swiss Data Science Center (SDSC), Andreasstrasse 5, Zurich, 8092, Switzerland – sequence: 7 givenname: Michael A. orcidid: 0000-0002-5000-2923 surname: Kraus fullname: Kraus, Michael A. organization: Institute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, Zurich, 8093, Switzerland |
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| Keywords | Generative AI Explainable AI Pedestrian bridge Conditional Variational Autoencoder Design space exploration Computational design |
| Language | English |
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| Title | Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-Model-Based Conceptual Design of Pedestrian Bridges |
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