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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Veröffentlicht in:Automation in construction Jg. 163; S. 105411
Hauptverfasser: Balmer, Vera, Kuhn, Sophia V., Bischof, Rafael, Salamanca, Luis, Kaufmann, Walter, Perez-Cruz, Fernando, Kraus, Michael A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2024
Schlagworte:
Computational design
Conditional Variational Autoencoder
Design space exploration
Explainable AI
Generative AI
Pedestrian bridge
Generative AI
Explainable AI
Pedestrian bridge
Conditional Variational Autoencoder
Design space exploration
Computational design
ISSN:0926-5805, 1872-7891
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Zusammenfassung: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.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2024.105411