Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges

• URL: http://arxiv.org/abs/2211.16406v1
• Date: Tue, 29 Nov 2022 17:28:31 GMT
• Title: Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
• Authors: Vera M. Balmer and Sophia V. Kuhn and Rafael Bischof and Luis Salamanca and Walter Kaufmann and Fernando Perez-Cruz and Michael A. Kraus
• Abstract summary: This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
• Score: 52.77024349608834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure optimisation methods, however, ignore qualitative aspects (e.g. aesthetics or construction methods). This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE), which serves as forward performance predictor for given design features as well as an inverse design feature predictor conditioned on a set of performance requests. The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland. Sensitivity analysis is employed for explainability and informing designers about (i) relations of the model between features and/or performances and (ii) structural improvements under user-defined objectives. A case study proved our framework's potential to serve as a future co-pilot for conceptual design studies of pedestrian bridges and beyond.

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