Automating Change Request Validation Using Industry Foundation Classes and Natural Language Processing

Construction projects generate massive amounts of information from initial feasibility study to decommissioning of facilities. Such information needs to be updated on a daily basis and is often stored in multiple incompatible file formats.
Unplanned changes and mismanagement of information cause delays and costly mistakes. To alleviate such issues, this paper investigates the possibility of using Industry Foundation Classes (IFC) data model and Natural Language Processing (NLP) to validate and visually identify the result of change requests. The approach builds upon a web-based platform capable of generating reports and visual previews, highlighting the differences between IFC files throughout the design processes.
The paper presents a web-based system prototype that allows users to compare different versions (old and new for example) of IFC design models in terms of: additions, modifications and deletions. The system uses NLP to intelligently identity the changes that have been made as it compares newer and older versions of the same model, making this information available to designers and 3D modellers.
Prospective work will focus on the application of artificial intelligence to automate the implementation of changes within the construction models.