  AI image generators based on diffusion models have recently garnered
attention for their capability to create images from simple text prompts.
However, for practical use in civil engineering they need to be able to create
specific construction plans for given constraints. This paper investigates the
potential of current AI generators in addressing such challenges, specifically
for the creation of simple floor plans. We explain how the underlying
diffusion-models work and propose novel refinement approaches to improve
semantic encoding and generation quality. In several experiments we show that
we can improve validity of generated floor plans from 6% to 90%. Based on these
results we derive future research challenges considering building information
modelling. With this we provide: (i) evaluation of current generative AIs; (ii)
propose improved refinement approaches; (iii) evaluate them on various
examples; (iv) derive future directions for diffusion models in civil
engineering.
