Text mining-based patent analysis of BIM application in construction

Abstract

As a data tool applicable to the full life-cycle of construction engineering and management, Building Information Modeling (BIM) has great potential for significantly increasing project productivity and performance. Awareness of BIM application hotspots and forecasting its trends can drive innovations in construction field. Using patents as data resources, this study develops an effective framework integrating the citation network analysis and the topic clustering technology to identify BIM application information and forecast its trends. This framework comprises three-step analysis:(1) quantitative characteristic analysis of patent outputs; (2) Social Network Analysis (SNA)-based co-occurrence network analysis; and (3) identification of BIM topics using a Latent Dirichlet Allocation (LDA). Finally, the case demonstrates the effectiveness of this framework contributing to promote technological development and innovation of BIM. The contributions of this study are threefold: (1) an innovative text mining-based framework for BIM patent analysis in construction is developed; (2) patents that have focused on identifying the application hotspots and development trend of BIM in accordance with our developed framework are reviewed; and (3) a signpost for technological development and innovation of BIM is provided.

Keywords

• Building Information Modeling (BIM)
• patent-driven analysis
• text mining
• Social Network Analysis (SNA)
• Latent Dirichlet Allocation (LDA)
• application hotspots and forecasting