Digital Twin Technology-driven Dynamic Monitoring and Early Warning System for Construction Progress

Abstract

Construction schedule delay problems are one of the most serious issues affecting the construction industry. Conventional construction schedule monitoring methods are limited by low data collection rates, multi-source information, and delayed response to schedule problems. Real-time deviation identification and early warning are difficult. This paper presents a digital twin-based technology for dynamic construction schedule monitoring and early warning. The system uses a four-layer structure to integrate BIM, IoT, and schedule management data to support sub-hourly continuous updates to the virtual construction site. At the deviation identification layer, a weighted composite deviation index method is developed by combining geometric construction ratio, resource consumption ratio, and time-based schedule performance index. The method uses trend extrapolation to create a dual-path deviation identification system. At the early warning layer, a three-level early warning protocol system (Blue, Orange, Red) with clear escalation paths and feedback mechanisms is developed. The system was applied to an eight-month period during the structural frame construction phase of a 28-story residential building project in eastern China. The results show that the system achieves a detection accuracy of 91.9%, an average detection time that is 87% less than the traditional 4D BIM method, and a cumulative schedule slippage that is 52% less. This study proves the feasibility of digital twin-based real-time construction schedule management. The study provides a reference for the application of digital twin-based intelligent schedule management technology in the construction industry.