A Bibliometric Analysis of AI Trends in the AEC Industry

Abstract

This study employs a comprehensive bibliometric analysis to examine the evolving landscape of Artificial Intelligence (AI) research within the Architecture, Engineering, and Construction (AEC) industry over the past decade. Through systematic analysis of 68 publications from the Scopus database, utilizing co-authorship networks, citation analysis, and keyword co-occurrence mapping, the research reveals significant patterns and trends in AI adoption and research focus. The findings indicate a rapid growth in research output, with China, the United States, and the United Kingdom emerging as leading contributors. The analysis identifies four primary research clusters: AI integration across AEC processes, building lifecycle applications, digital technologies convergence, and automation techniques. A temporal evolution is observed, transitioning from basic automation to sophisticated applications involving machine learning, digital twins, and deep learning. The study highlights geographical disparities in research contributions and emphasizes the need for standardization in AI implementation. By providing insights into research trends, collaborative networks, and evolving focus areas, this analysis contributes to a deeper understanding of AI's role in transforming the AEC industry. The findings can guide future research directions, inform industry practitioners about emerging technologies, and support policymakers in developing frameworks for AI adoption in construction, ultimately facilitating more effective and responsible integration of AI technologies in AEC practices.