In-situ monitoring of graphene-reinforced mortar under loading using a vibro-acoustic Technique: A theoretical, numerical, and experimental study

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This study explores the application of the amplitude-modulated vibro-acoustic (AMVA) technique for in-situ monitoring of structures under compressive loads. The AMVA technique employs an amplitude-modulated pump wave combining two low frequencies, fL1 and fL2 (fL1<fL2). While this approach allows for the transmission of signal at fL2, it also facilitates the transmission of fL1, corresponding to the natural frequency of the structure. This amplitude-modulated pump wave breaks the limitation of signal emitters by extending the frequency range, making AMVA more versatile. Additionally, the amplitude-modulated pump wave has lower energy consumption, making AMVA particularly suited for long-term structural health monitoring.Despite these advantages, in-situ monitoring using AMVA is limited by environmental noise and the complexity of damage patterns, which can reduce its sensitivity and accuracy in detecting damage. To address these limitations, this study provides a comprehensive investigation involving theoretical, numerical, and experimental studies. The theoretical and numerical studies focus on understanding changes in the nonlinear parameter β as it propagates through discontinuous media. Furthermore, experimental investigations, supported by machine learning techniques, aim to interpret the complex patterns of β under real damage scenarios. Machining learning enhances the accuracy and reliability of β as a predictor for structural damage.Experimental comparisons with pulse velocity, LVDT displacement measurements, and strain data obtained via DIC confirmed that β is three to four orders of magnitude more sensitive than conventional displacement measurements. Its significant correlation with strain across multiple cracks highlights the potential of β as a predictive tool for structural damage. This research demonstrates the potential of the AMVA technique in in-situ structural health monitoring but also suggests its possible integration with machine learning for developing maintenance strategies.