## 6 Conclusions and Future Work  
This paper conducted an experiment on generating architectural floor plans for standard nursing units in general hospitals using the SD model. The experiment successfully demonstrated that clear, readable, and functionally reasonable floor plan images with colored functional blocks can be quickly and automatically generated by inputting a boundary range. The experiment validated the methods for dataset processing, model training, and the overall effectiveness of the entire workflow.  
Under conditions with relatively few training samples, the SD model-based method for generating architectural floor plans exhibited advantages such as reasonable layout, strong generalizability, and clear, easily readable image boundaries. When the input conditions include significant spatial features of the floor plan type, the model's generalization capability is even more pronounced, resulting in better generation outcomes.  
Using deep learning to generate architectural floor plans is not the only approach to floor plan generation, but it still demonstrates significant potential for research applications. Although the generated color block images do not represent traditional floor plans in a practical sense, they can provide valuable insights for architects during the building design phase.  
The limitations of this experiment are quite evident. The only effective control parameters are the boundaries of the nursing unit and the main corridor. While LoRA can generate functionally coherent layouts, it also introduces randomness. Additionally, quantitative metrics such as room area, room scale, and room count, which are of great concern to architects, are difficult to represent at the functional layout level. This is inherently tied to the operational mode of the Stable Diffusion image-generation process. Future work in this research aims to explore the text-based control capabilities of the Stable Diffusion model to actively participate in the generation process. Furthermore, we will vectorize the output functional block images and automate the layout for each functional room, allowing the generated results to be quantitatively represented and more closely aligned with architects' workflows.  
In the current study, we focus primarily on the application of AI tools in the architectural design process, particularly in the generation of standard floor plans for nursing units in general hospitals. However, if the scope of research were to be expanded to other complex building types, engineering design requirements such as structural safety and MEP (Mechanical, Electrical, and Plumbing) system configurations would also be crucial. Although the emphasis of this study is on architectural design, the proposed methods also hold potential for integration with engineering design. AI tools can learn from images of engineering design layouts to provide recommendations on aspects such as structural loads and system configurations, thereby optimizing the overall design. This approach can assist architects in anticipating and addressing potential engineering issues at the early design stage, enhancing the scientific basis and feasibility of the design. As AI technology in architectural design is still in its early stages, future research should further explore how to better integrate AI with engineering design needs to improve overall design quality and practicality.  
Conceptualization, Z.H. and Y.C.; Methodology, Y.C.; Validation, Y.C.; Resources, Z.H.; Data curation, Z.H.; Writing--original draft, Z.H.; Writing--review & editing, Z.H.; Supervision, Y.C.; Funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Beijing Jiaotong University Horizontal Project of Social Sciences (No. A24 SK00020): Big Data Management System Project for Medical Building Space.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.