Generating Initial Digital Twin Models for the Operation of Road Tunnels

Abstract: Ensuring the safe and uninterrupted operation of tunnels necessitates continuous monitoring and maintenance. Digital methods, including digital twins, have proven their effectiveness in reducing operating costs but require digital models that are often unavailable for existing tunnels.Therefore, this paper conceptualizes a deep learning-based approach to transform unstructured tunnel documentation into digital models through three core modules: (1) reconstructing the tunnel geometry from construction drawings, (2) analyzing inspection reports to create a damage model for structural condition assessment, and (3) analyzing point clouds, videos, and product data sheets to model the tunnel ventilation system.