A Data-Driven BIM Framework for Digital Twin Integration with ISO 23247-Compliant Automation in Construction

The increasing complexity of today's construction projects makes advanced data management and interoperability solutions essential for optimizing decision-making processes, ensuring regulatory compliance, and enabling real-time monitoring. While traditional BIM methods are effective in terms of graphical visualization, they lack structured parametric and regulatory data integration, which limits their potential for synchronization with digital twin systems. This data fragmentation leads to inefficiencies in automation processes, reducing the effectiveness of predictive analytics and lifecycle adaptability. To address this gap, this study presents a BIM framework based on the ISO 23247 standard, aiming to achieve structured data management and digital twin integration by systematically classifying and organizing Graphical, Non-Graphical, and Document Data. The proposed framework enhances BIM's functionality as an intelligent asset management system by increasing interoperability, enabling automated compliance verification, and strengthening sensor-driven analysis. Industry case studies validate the framework's adaptability across design models, regulatory documents, and predictive analyses, and demonstrate its scalability in digital construction environments. Additionally, this study highlights the role of AI-powered compliance automation in optimizing regulatory oversight and operational efficiency and examines its potential for industry-wide standardization. Future research should focus on expanding digital twin applications, integrating AI-powered automation, and developing structured BIM methods. This study provides a solid foundation for data-driven construction management by aligning BIM workflows with ISO 23247, ensuring long-term scalability and efficiency.