Experimental investigation on buried pipeline bending deformation monitoring using flexible piezoelectric sensing

• A distributed monitoring method for buried pipelines using SPGC is proposed. • SPGC is based on the piezoelectric effect and electromechanical impedance technology. • Voltage and impedance signals correlate linearly with pipeline deformation. • Consistent signal trends are observed in overhead and buried pipeline tests. • SPGC enables early localization, precursor identification and graded warnings for buried pipeline damage. Buried bending deformation and failure are the most critical threats to pipeline structural integrity, making real-time deformation monitoring essential. This study presents a novel monitoring approach using a sensor-enabled piezoelectric geocable (SPGC) to assess buried pipeline bending deformation. Overhead and buried pipeline experiments were conducted under dynamic loading conditions using a dynamic-static test system. Monitoring performance was evaluated using voltage and impedance signals generated by the SPGC. The overhead pipeline tests demonstrated that impedance signals accurately characterize deformation states, with the bending degree exhibiting a proportional relationship with normalized impedance variation. The tensile side provided the most reliable deformation data. For buried pipelines subjected to simulated vehicle loads, fully-loaded vehicles generated voltage signals 2.5 times higher and normalized impedance variations 1.8 times greater than those from empty vehicles at identical speeds. Furthermore, generalized voltage-speed and impedance–strain calculation models were derived through laboratory testing. Unlike conventional strain gauges limited by spatial resolution and power dependence, the SPGC system enables real-time, distributed, and energy-autonomous monitoring of buried pipeline deformation. These findings demonstrate the potential of the SPGC-based monitoring technique for long-term buried pipeline integrity assessment, providing critical deformation data for structural health assessment.