Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing. (October 2021)

• Record Type: Journal Article
• Authors: Abdirad, Hamid; Mathur, Pegah
• Is Part Of: Advanced engineering informatics. Volume 50(2021)
• Publication Date: 2021-10
• Subjects: BIM -- Content management -- Recommender systems -- Machine learning -- Association rules; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101414
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30, 000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.