Brillouin fiber optic sensors and mobile augmented reality-based digital twins for quantitative safety assessment of underground pipelines

The impossibility of visual inspection and the complexity of combined loads hamper the quantitative assessment, lifetime prediction and control of underground pipelines during their lifecycles. A methodology based on mobile augmented reality (MAR) and Brillouin fiber optic sensors (BFOSs) is presented to build a digital twin (DT) for underground pipelines. Field experiments were carried out to demonstrate that the proposed method can quantitatively assess and predict the structural safety of an underground pipeline from the DT. The results demonstrate that the distributed sensor networks can measure important but unpredictable deformations (i.e., longitudinal bending and axial thermal strain), the joint data-physics driven model can estimate the structural stress state more accurately than the common calculation model, and the MAR-based human-asset interaction interface enables more intuitive, efficient, automated operation and maintenance (O&M). In the future, in-line robotic systems and localized damage models should be further adopted for lifecycle O&M.

Automated summary

This paper presents a method for building a digital twin of underground pipelines using mobile augmented reality and Brillouin fiber optic sensors. Field experiments demonstrate the feasibility of the proposed method, showing that it can accurately assess and predict the structural safety of underground pipelines, enabling more efficient operations and maintenance.