Information Extraction for Semantic Enrichment of BIM for Bridges

Current bridge projects mainly rely on PDF plans as the official deliverables and documents to be stored, communicated, and transferred among different stakeholders. With the Industry Foundation Classes' (IFC) Building Information Modeling (BIM) standard adopted by the American Association of State Highway and Transportation Officials (AASHTO) as the national standard for modeling bridge and road infrastructure projects, upgrading the documentation of bridge projects to 3D BIM in compliance with the national standard has become an urgent need. In this research, the state-of-the-art PDF2BIM algorithms were leveraged to semi-automatically create 3D geometric models of bridges based on PDF drawings. To enrich the 3D geometric model with semantic information of bridges' components (e.g., bridge name, structure type, concrete strength), information extraction algorithms based on optical character recognition (OCR) and natural language processing (NLP) were developed to extract data from the bridge plans automatically. It significantly increases the efficiency and productivity of information extraction and enrichment of IFC-based BIM instance models for bridges by leveraging the rich information that already resides in the PDF plans. The results show that it achieved 97.6% accuracy in the information extraction task and reduced the overall time consumption on processing bridge data by 96.3% compared to the manual approach.