AI Image generators based on diffusion models are widely discussed recently
for their capability to create images from simple text prompts. But, for
practical use in civil engineering they need to be able to create specific
construction plans for given constraints. Within this paper we explore the
capabilities of those diffusion-based AI generators for computational design at
the example of floor plans and identify their current limitation. We explain
how the diffusion-models work and propose new diffusion models with improved
semantic encoding. In several experiments we show that we can improve validity
of generated floor plans from 6% to 90% and query performance for different
examples. We identify short comings and derive future research challenges of
those models and discuss the need to combine diffusion models with building
information modelling. With this we provide key insights into the current state
and future directions for diffusion models in civil engineering.

基于扩散模型的 AI 图像生成器近期广泛讨论，具备从简单文本提示中创建图像的能力。但是，在土木工程的实际应用中，它们需要能够根据给定的约束条件创建特定的施工计划。本文通过探索基于扩散的 AI 生成器在计算设计中的能力，以楼层平面图为例，辨识其目前的局限性。我们解释了扩散模型的工作原理，并提出了具备改进语义编码的新扩散模型。通过多个实验证明，我们可以将生成的楼层平面图的有效性从 6% 提高至 90%，并提高不同示例的查询性能。我们对这些模型的不足之处进行了辨识，并得出了未来的研究挑战，并讨论了将扩散模型与建筑信息模型相结合的需求。通过这些，我们提供了土木工程中扩散模型的当前状态和未来方向的关键见解。