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.

翻译：基于扩散模型的AI图像生成器因其能够根据简单文本提示生成图像的能力而备受关注。然而，在土木工程的实际应用中，这些生成器需要能够针对给定约束条件生成具体的施工方案。本文研究了当前AI生成器在应对此类挑战方面的潜力，特别是针对简单平面图的生成。我们阐释了底层扩散模型的工作原理，并提出新颖的优化方法以改进语义编码和生成质量。通过多项实验证明，我们将生成平面图的有效性从6%提升至90%。基于这些结果，我们提出了考虑建筑信息建模的未来研究挑战。具体贡献包括：（i）评估当前生成式AI模型；（ii）提出改进的优化方法；（iii）在多个实例中评估这些方法；（iv）推导扩散模型在土木工程领域的未来发展方向。