Data-driven generative contextual design model for building morphology in dense metropolitan areas

Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing designs. This paper aims to develop a data-driven generative design model: VmRF, which can learn from existing designs and generate plausible and contextual building morphologies. The model consists of a variational autoencoder (VAE) to compress high-dimensional building morphology datasets into low-dimensional building morphology datasets and a multivariate random forest (mRF) to identify explainable relationships between design parameters and morphology patterns. Performance evaluation shows the superiority of the VmRF model in terms of training speed and prediction fitness. Consequently, the proposed model promotes enhanced design efficiency, innovation in contextual awareness, and evidence-based decision-making in building morphology design.