Generative Artificial Intelligence in Architecture, Engineering, Construction, and Operations: A Systematic Review

Generative artificial intelligence (GenAI) is a tool that can be applied to virtually all aspects of business and life, including the construction industry. However, the adoption of GenAI in the construction industry, as with other innovations, is slow, and many of its applications thus far have been rather simplistic or failed to deliver a useful, credible output. There is a limited understanding of how GenAI is adopted in current practice and its potential to improve future practice in architecture, engineering, construction, and operations (AECO). Using a systematic literature review approach, this study aims to map the current issues in applying GenAI. The literature review initially identified 1013 peer-reviewed articles from ProQuest, Scopus, and Web of Science. The articles were further filtered based on specific criteria, resulting in 28 articles being retained for thematic analysis. The findings show a cluster of patterns in which GenAI is being adopted and shows promise. The core themes identified are as follows: (1) project brief, (2) architectural design, (3) building information modelling, (4) structural design, (5) construction and demolition, (6) operations, and (7) urban governance. A typical trend noted in the AECO industry has been training AI models that achieve quicker results, improve quality, and use fewer resources.