| Title |
Trends in Research and Technological Advances in Generative Design Technology for Architecture, Engineering, and Construction(AEC) |
| Authors |
창쩌위안(Chang, Ze-Yuan) ; 양호(Yang, Hao) ; 한정원(Han, Jeong-won) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.1.127 |
| Keywords |
Architecture Engineering and Construction; Generative Design Technologies; Architectural Design; Research Trends |
| Abstract |
Generative Design Technologies (GDT) in Architecture, Engineering, and Construction (AEC) are advancing quickly, but the evidence remains
scattered and lacks a unified overview. This study uses a PRISMA-guided bibliometric and science-mapping approach to analyze Web of
Science and Scopus publications from 2020?2024. Tools such as Citespace, Bibliometrix, and VOSviewer help reveal knowledge structures,
research trends, and evolving patterns. Research output grew from 46 to 270 papers over five years, with China and the United States
leading contributions. Evidence from practical applications shows clear benefits, including improved architectural design optimization and
alternative generation, smarter and more automated construction workflows, and enhanced building energy-performance evaluation. Findings
suggest a move toward an integrated generative design technology stack combined with BIM as the data backbone, supported by Deep
Learning and Machine Learning for life-cycle decision-making. Since 2022, GDTs in the AEC sector have increasingly merged BIM,
AI-driven analytics, digital twins, and lifecycle data interoperability, building a foundational framework that supports sustainability, energy
efficiency, and intelligent design. Overall, this study offers an evidence-based foundation for expanding GDT use and promoting sustainable
building outcomes through data-driven decision support. |