Dynamic Monitoring and Analysing Method of Construction Project Performance Based on Digital Twin

Higher-level management and monitoring systems must be developed to maintain increasing global civil infrastructure projects. One such innovation in the construction industry is the Digital Twin (DT), which helps businesses address the evolving needs for smart city development and infrastructure growth through enhanced project management and execution. In this paper, we introduce a DT framework that will digitally describe construction areas in real-time through the use of Internet of Things (IoT) sensors, Building Information Modelling (BIM), and machine learning (ML). The computer program periodically collects and analyses data using sensors associated with the environment along with 3D imaging sensors (LiDAR, photogrammetric cameras). More Effective project management and monitoring are the result of linking this data with BIM systems, allowing for reports on the construction development. In order to promote proactive management, this system uses ML algorithms to analyze patterns and predict future risks. For superior prediction and decision-making by the DT, this system's dual-input layout processes both spatial and temporal data. The framework enables a method to collect data in real time and use them to achieve early detection of issues, improved resource management, and enhanced project outcomes.