Applications of machine learning to BIM: A systematic literature review. (January 2022)

• Record Type: Journal Article
• Authors: Zabin, Asem; González, Vicente A.; Zou, Yang; Amor, Robert
• Is Part Of: Advanced engineering informatics. Volume 51(2022)
• Publication Date: 2022-01
• Subjects: Building information modeling -- Artificial intelligence -- Machine learning -- Data mining -- Systematic literature review; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101474
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: As Building Information Modeling (BIM) workflows are becoming very relevant for the different stages of the project's lifecycle, more data is produced and managed across it. The information and data accumulated in BIM-based projects present an opportunity for analysis and extraction of project knowledge from the inception to the operation phase. In other industries, Machine Learning (ML) has been demonstrated to be an effective approach to automate processes and extract useful insights from different types and sources of data. The rapid development of ML applications, the growing generation of BIM-related data in projects, and the different needs for use of this data present serious challenges to adopt and effectively apply ML techniques to BIM-based projects in the Architecture, Engineering, Construction and Operations (AECO) industry. While research on the use of BIM data through ML has increased in the past decade, it is still in a nascent stage. In order to asses where the industry stands today, this paper carries out a systematic literature review (SLR) identifying and summarizing common emerging areas of application and utilization of ML within the context of BIM-generated data. Moreover, the paper identifies research gaps and trends. Based on the observed limitations, prominent future research directions are suggested, focusing on information architecture and data, applications scalability, and human information interactions.