  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.
