Digital Twin-Enabled Composite Liner System for Automatic Dent Detection with iFEM-based Deformation Visualization and Performance Prediction in Pipelines

This study develops a smart composite liner system and a dedicated digital twin framework integrated with the liner to enable internal protection, continuous deformation monitoring, and predictive assessment of dented pipelines. The composite liner integrates distributed strain sensing, where localized indentation generates strain signatures that are converted into digital twin representations of the dented pipe. Digital twin generation is enabled by two specialized algorithms developed for discrete fiber-path measurements and cylindrical pipeline geometry. First, a machine-learning-enhanced Thin Plate Spline (ML-TPS) approach reconstructs full-field circumferential strain distributions from sparse measurements while retaining sharp strain gradients near the dent. The ML-TPS framework uses feature-engineered strain–depth curves and ensemble learning to predict dent depth and circumferential offset, then applies ML-assisted spline reconstruction to predict strain at irregular circumferential positions. Second, a developed strip-based KLPT iFEM formulation converts the reconstructed strain field into three-dimensional dent geometry. The pipe circumference is partitioned into angular strips aligned with the pipe axis for curvature-based reconstruction, and the strip solutions are merged into a continuous cylindrical surface to recover the full geometry of the dented pipeline. Two practical sensor layouts integrated into the liner, 4-path and 3-path, are evaluated to balance reconstruction fidelity and deployment cost. Displacement-controlled indentation tests with 3D scanning validation demonstrate reliable dent depth reconstruction from 1 mm to 24 mm, corresponding to 12% of pipe outer diameter. The reconstructed geometry supports FEA-based structural capacity assessment, CFD-based flow performance evaluation, and remaining useful life estimation for predictive maintenance.