Towards smart maintenance for operational tunnels: from digital reconstruction to predictive analysis

This study presents an innovative maintenance strategy for in-service tunnels, integrating high-fidelity 3D digital reconstruction with physics-based simulation to enable predictive maintenance. First, a digitalisation workflow is developed to integrate geological conditions, tunnel structures, and defect data using 3D point-matching, semantic extraction, and Kriging interpolation. Statistical analysis then unveils correlations between structural defects and geological conditions. Furthermore, a novel bottom-up automatic mesh-processing workflow is presented to automate mesh partitioning, quality assessment, and topology optimisation in digital models, enabling seamless transitions from digital models to numerical simulation. A case study of the Yutang Liangzi Tunnel in China is conducted to verify the proposed strategy. The developed digital model discovers significant correlations between tunnel defects and geological conditions. Finite element analysis reveals stress patterns in tunnel structures under various boundary and load conditions. The proposed strategy shows potential to advance next-generation maintenance practices for operational tunnels.