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

TL;DR

This paper introduces a CVAE-based framework that enhances pedestrian bridge conceptual design by enabling performance prediction and inverse design, integrating qualitative and quantitative aspects for more efficient exploration.

Contribution

It presents a novel CVAE approach for performance-driven design exploration that combines forward prediction, inverse design, and explainability in pedestrian bridge conceptual design.

Findings

Successfully trained on 18,000 synthetic bridge instances

Demonstrated ability to predict performance metrics from design features

Enabled inverse design conditioned on performance requests

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.