There are high-risk problems, such as peripheral rock instability and palm face collapse, during tunnel construction, and the traditional monitoring methods are difficult to meet the safety management needs due to sparse data and lagging response. This paper proposes an intelligent monitoring system for the whole process of tunnel construction based on Internet of Things (IoT) and digital twin, which integrates a multi-source sensor network, BIM dynamic modeling, and risk intelligent analysis. The system realizes an all-around perception of environment, equipment, and surrounding rock status through real-time fusion of heterogeneous data, and uses digital twin technology for 3D visualization and risk trend prediction. It adopts the improved D-S evidence theory for multi-source risk assessment, and improves the early warning accuracy through the effectiveness factor and conflict weakening strategy. The actual engineering experiments show that the system achieves 100% monitoring coverage and 100% warning accuracy, and successfully captures the whole process of palm face collapse and the time-sequence evolution of enclosing rock deformation, which significantly improves the safety and management efficiency of tunnel construction. The study verifies the high accuracy and stability of the proposed system, which provides an intelligent solution for the safety of complex underground projects.
Huang et al. (2026) studied this question.