Artificial Intelligence in Building Information Modeling Research: Country and Document-based Citation and Bibliographic Coupling Analysis

Abstract

The intense association of the architecture, engineering, construction, operation, and facility management (AECO/FM) industry with cognitive and behavioral technologies leads to the increase in productivity of industry activities. In light of these thoughts, the building information modeling (BIM) platform is included in the AECO/FM industry to further increase efficiency and deliver construction projects economically, timely, and safely. While the BIM platform can work integrated with many programs and systems, concepts that offer innovative and fast solutions such as artificial intelligence (AI) benefit the AECO/FM industry. The main aim of this study is to understand the tendency of AI in BIM research carried out in different countries and by various scholars. This study adopts a bibliometric search, and a scientometric analysis and mapping approach with applying document-based citation analysis, country-based citation analysis, and country-based bibliographic coupling analysis of scientific research of AI and BIM integration. Data on the use of AI and BIM has been collected by reviewing and screening articles selected from the Scopus database. The results reveal that information management, decision support systems, genetic algorithms, neural networks, knowledge-based systems, machine learning, and deep learning effect AI in BIM research. This article contributes to the AECO/FM literature by analyzing and visualizing the current status and relationship between AI and BIM. Therefore, the findings highlight the gaps and trends in AI and BIM studies and provide new recommendations for future studies.