Automatic Compliance Checking of BIM Models for Architecture and Fire Protection Based on Knowledge Graphs and Machine Learning

Compliance checking of Building Information Modeling (BIM) models is a critical process throughout the construction lifecycle, particularly during the design phase. Building design often involves the integration of multiple disciplines and complex spatial relationships, leading to errors. The growing volume and complexity of information embedded in BIM models have further complicated compliance checking. Traditional manual methods are not only time-consuming but also prone to mistakes. To address these challenges, this study proposes an integrated conceptual framework for automated BIM compliance checking, leveraging knowledge graph (KG) and machine learning. The framework aims to convert unstructured clauses in Chinese building standards into structured, interpretable, and extractable data, enabling the automatic detection of design errors in BIM models. The framework incorporates several key components. First, it constructs a knowledge graph by developing ontologies for Chinese building standards and training semantic role annotation models. A data extraction pipeline is designed using the Dynamo module in Revit to retrieve relevant information from BIM models. Finally, compliance checking logic is defined using Java to establish rules for matching the extracted building standard knowledge with BIM model information. The feasibility of this automated compliance-checking framework was validated using BIM models from two real-world projects, demonstrating its potential to streamline the compliance process and reduce errors in building design