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

ABSTRACTThis 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.