Abstract To address the challenges of acquiring water-richness information in surrounding rock during tunnel construction and the lack of timely early warning for water-inrush risks, this study proposes a digital twin and risk decision-making framework for evaluating the water-richness of surrouding rock ahead of tunnel face. The framework achieves virtual mapping of rock water-richness through point cloud modeling and introduces a fuzzy cloud probability model for refined risk assessment of water-inrush per meter. The framework comprises three key steps: (1) Acquisition of rock resistivity data via transient electromagnetic method (TEM) with 3D spatial registration to construct a water-richness database; (2) Generation of forward point cloud models based on tunnel face geometry, where Knearest neighbors (KNN) algorithm assigns water-richness attributes to establish a digital twin of water-richness of surrounding rock; (3) Uncertainty quantification of the digital twin using the fuzzy cloud probability model for intelligent water-inrush risk assessment. Application in Yanjiazhai Tunnel demonstrates that the framework enables rapid extraction of water-richness information and virtual mapping through digital twins. Comparative analysis with actual water-inrush conditions during excavation verifies the accuracy of the proposed fuzzy cloud probability model in risk evaluation of water inrush ahead of tunnel face.
Abstract：
To address the challenges of acquiring water-richness information in surrounding rock during tunnel construction and the lack of timely early warning for water-inrush risks, this study proposes a digital twin and risk decision-making framework for evaluating the water-richness of surrouding rock ahead of tunnel face. The framework achieves virtual mapping of rock water-richness through point cloud modeling and introduces a fuzzy cloud probability model for refined risk assessment of water-inrush per meter. The framework comprises three key steps: (1) Acquisition of rock resistivity data via transient electromagnetic method (TEM) with 3D spatial registration to construct a water-richness database; (2) Generation of forward point cloud models based on tunnel face geometry, where Knearest neighbors (KNN) algorithm assigns water-richness attributes to establish a digital twin of water-richness of surrounding rock; (3) Uncertainty quantification of the digital twin using the fuzzy cloud probability model for intelligent water-inrush risk assessment. Application in Yanjiazhai Tunnel demonstrates that the framework enables rapid extraction of water-richness information and virtual mapping through digital twins. Comparative analysis with actual water-inrush conditions during excavation verifies the accuracy of the proposed fuzzy cloud probability model in risk evaluation of water inrush ahead of tunnel face.
XU Caijian1 CHEN Xingyu1 LEI Minglin1 ZHANG Xinglong2 SUN Huaiyuan2 LI Xiaojun2
.Digital Twin and Risk Decision-making for Water-richess of Surrounding
Rock Ahead of Tunnel Face[J] MODERN TUNNELLING TECHNOLOGY, 2025,V62(4): 90-99
2025, Vol. 62 