Semantic Enrichment of BIM: The Role of Machine Learning-Based Image Recognition

Abstract

Building Information Modelling (BIM) revolutionizes the construction industry by digitally simulating real-world entities through a defined and shared semantic structure. However, graphical information included in BIM models often contains more detailed data compared to the corresponding semantic or computable data. This inconsistency creates an asymmetry, where valuable details present in the graphical renderings are absent from the semantic description of the model. Such an issue limits the accuracy and comprehensiveness of BIM models, constraining their full utilization for efficient decision-making and collaboration in the construction process. To tackle this challenge, this paper presents a novel approach that utilizes Machine Learning (ML) to mediate the disparity between graphical and semantic information. The proposed methodology operates by automatically extracting relevant details from graphical information and transforming them into semantically meaningful and computable data. A comprehensive empirical evaluation shows that the presented approach effectively bridges the gap between graphical and computable information with an accuracy of over 80% on average, unlocking the potential for a more accurate representation of information within BIM models and enhancing decision-making and collaboration/utility in construction processes.