Artificial Intelligence to Facilitate the Conceptual Stage of Interior Space Design: Conditional Generative Adversarial Network-Supported Long-Term Care Space Floor Plan Design of Retirement Home Buildings

ABSTRACT

This study uses Conditional Generative Adversarial Network (CGAN) to construct a method for generating floor plans for long-term care spaces in retirement home buildings to assist architects in improving interior space design. The results of this study show the following: (1) For the interior design of long-term care spaces in retirement home buildings, the CGAN model has strong understanding and calculation capabilities. The zoning layout of long-term care spaces in retirement home buildings has been completed, and the results show that the CGAN model has reference value. (2) Although there are several differences in the design of CGANs and authentic design, there are still many similarities. Some unreasonable results, such as space generation in corridors and elevator shafts, require further manual correction. (3) According to a later questionnaire survey on the satisfaction of architects and CGAN model design solutions, the difference between the two is not large, which also illustrates the great potential of CGANs for intervention in interior space design. This helps architects create more detailed plans based on the model, greatly increasing work efficiency. Moreover, additional interior space design possibilities can be explored, and to some extent, the architect’s subjective assumptions can also be corrected.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Author Contributions

Yanyu Li wrote the first draft, drew preliminary illustrations, and designed the research questionnaire; Huanhuan Chen revised the drawings, drew the 3D model during the experiment, and analyzed the background of the research. Jingyi Mao was deeply involved in the early stages of the survey and the digital processing of samples. Yile Chen and Liang Zheng revised the first draft, constructed the entire research idea, and translated and revised the English. Liang Zheng conducted the training of the machine learning model. Huanhuan Chen, Junjie Yu, and Lulu He conducted extensive questionnaire surveys and statistics. Junjie Yu sorted out the literature review of the study and wrote the analysis of the questionnaire results. Lina Yan provided technical assistance throughout the research process. Jingyi Mao, Yile Chen, and Liang Zheng completed the drawing and revision of the paper’s illustrations, primarily producing three revised versions of the article. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original code of the program cannot be released yet because our program is being used in other research. The training data set can be downloaded here: https://data.mendeley.com/datasets/6j9fpjc7yd/1

Supplementary Data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/08839514.2024.2354090

Additional information

Funding

This paper is the 2023 Qingyuan City philosophy and social science planning project, Research on the construction of community service system for rural enjoyment of elderly life in Qingyuan City under the background of rural revitalization（Project Number: QYSK2023129）and Guangdong Provincial Department of Education’s key scientific research platforms and projects for general universities in 2023: Guangdong, Hong Kong, and Macao Cultural Heritage Protection and Innovation Design Team (Funding Project Number: 2023WCXTD042).