A systematic risk identification framework based on digital twin

In construction projects, risks are inevitable and may arise dynamically throughout the project lifecycle. This study proposes a novel risk identification framework for engineering projects based on Digital Twin (DT) technology, integrating static and dynamic methods to combine objective data analysis with expert judgment. By leveraging DT’s real-time monitoring and proactive capabilities, the framework enhances risk visualization and supports informed decision-making in complex project environments. Its effectiveness was validated through a case study covering construction and operation phases, with results confirmed by on-site personnel. SWOT and comparative analyses with existing methods highlight the framework’s strengths and improvements in risk identification accuracy and decision-making support. The framework improves traditional risk management by leveraging DT for real-time monitoring, visualization, and enhanced decision-making in complex engineering projects, which provides a novel solution for risk identification.