Generative AI is bringing revolutionary changes to architectural design. From data-driven and sustainable perspectives, this study introduces scientific data analysis methods to explore the specific application scenarios and effectiveness of generative AI in the early, middle, and late stages of architectural project design, while also examining its potential value in the field of sustainability. The research first synthesizes industry viewpoints through online data analysis. Secondly, it selects three typical practical architectural projects of different scales and types in which the author participated in comparative testing, recording the time, operational processes, and outputs required for schemes generated by the “traditional creative workflow” vs. the “AI-assisted workflow” at various stages. A multi-dimensional evaluation is conducted combining subjective questionnaires and objective performance simulation data. This study finds that generative AI can significantly enhance design efficiency and scheme diversity and guide the construction of sustainability dimensions, but challenges exist in quality control and technology integration. This research will provide an empirical framework and data benchmarks for architectural practitioners, clarifying a new design path of “data-driven–human–machine collaboration–sustainable optimization”, which holds significant reference value for promoting the transformation of the construction industry towards high efficiency and low carbon.
A Thu, study studied this question.