Dynamic integration of unstructured data with BIM using a no-model approach based on machine learning and concept networks

Accessing unstructured information through BIM-based platforms is essential for achieving integrated analytics especially, for facilities management where a wide range of unstructured data is required for effective decisionmaking. Previous research has explored linking textual data with BIM through the use of relational schemas, concept mapping, and ontologies. However, these methods are structured and static, failing to match the nonparametric and evolutionary nature of unstructured data. This study proposes an alternative approach where concept networks are used to represent the IFC data model. Using graph theory and natural language processing a classifier is trained for assigning text documents to their relevant IFC classes based on their conceptual network distances. Given that the classifier is trained on conceptual distances rather than the concepts themselves, it has the potential to be generalized to unseen classes with unseen concepts. Both the performance and the generalizability of the approach are evaluated in a case study.