Electromechanical behavior of graphene-based multifunctional composites under quasi-static indentation and low-velocity impact tests aided by XCT analysis

Fiber-reinforced polymer composites (FRPCs) are highly efficient structural materials, yet they remain susceptible to subsurface damage caused by out-of-plane loading. Carbon-based nanomaterials have been extensively explored for intrinsic piezoresistive sensing under tensile and flexural loading, but their electromechanical response under indentation and impact remains less well established. In this study, the piezoresistive behavior of reduced graphene oxide (rGO)-coated glass-fiber laminates is examined through quasi-static indentation (QSI) and low-velocity impact (LVI) tests, complemented by X-ray Computed Tomography (XCT) to characterize the extent and morphology of internal damage. The QSI tests included loading to failure, sustained creep, and creep following cyclic conditioning, while the LVI tests were performed at multiple energy levels to induce different damage severities. Under monotonic QSI, sensors placed directly beneath the indenter recorded a sharp fractional change in resistance (FCR) drops up to –90%, compared to <12% in transverse sensors, with damage initiation being observed at a cross-head displacement ∼5.5–6 mm . Creep tests highlighted gauge length effects, and cyclic preconditioning reduced the peak FCR by more than 50% (from ∼13% to 6%) while stabilizing the piezoresistive response. During LVI, the peak forces reached ∼9.5 kN within ∼3 ms during a 100 J impact, and the sensors continued to capture delamination and fiber fracture even after perforation. XCT scans confirmed the progression of subsurface delamination and fiber breakage, providing a direct link between electromechanical signals and internal damage mechanisms. These findings demonstrate the potential of rGO-based sensing layers in detecting out-of-plane damage across diverse loading conditions and contribute to the development of multifunctional composites for structural health monitoring.