Structural evaluation of cracked shield tunnels using computer-vision-based model updating techniques

Accurate and efficient assessment of structural damage in shield tunnels is essential for ensuring the safety and reliability of transportation systems. Cracks in tunnel linings are common, necessitating regular structural integrity assessments to ensure safety. Traditional modeling of such damage is often complex and time-consuming. Therefore, the objective of this study is to automate the entire process from detecting tunnel damage in images to conducting numerical analyses for shield tunnels, thereby enabling rapid assessment of structural integrity. We propose a segment-based method that updates a finite element (FE) model of shield tunnels to reflect geometric changes due to cracks, utilizing computer vision (CV) techniques and geometric analyses. Firstly, the Segment Anything Model, along with CV techniques, is used to identify the shapes and sizes of tunnel components from full and partial tunnel segment images. Then, a Dual VMamba U-Net (DVMamba-UNet) is proposed to identify cracks and provide detailed crack information, i.e., crack masks. Finally, geometric analysis is employed to develop algorithms that automatically transform coordinates and select elements within FE models, facilitating the update of geometric changes. Residual capability assessments of updated FE models are used to evaluate the structural damage and the tunnel segment condition. Two case studies are conducted to verify the effectiveness of the proposed approach and algorithms. The results show that the proposed method allows for automatic updates to the FE tunnel model based on damage detected in images through CV techniques and geometric analyses. Additionally, updated FE tunnel models representing different damage levels are developed and analyzed using numerical simulations. This approach not only proves effective in evaluating structural damage in shield tunnels but also offers potential as a data processing and model updating modules within future Digital Twin frameworks for tunnel infrastructure.