Optimizing Bridge Health Monitoring with Acoustic Emission Techniques

The increasing need for reliable Structural Health Monitoring (SHM) of bridge infrastructures has brought attention to the use of Acoustic Emissions (AE) as a promising and effective technique. This study presents a data-driven approach to bridge SHM, focusing on the extraction of features from AE signals for the automatic identification of damage using advanced signal processing and machine learning techniques. This methodology enables the early detection of structural issues, ensuring timely maintenance and reducing the risk of catastrophic failures. The proposed method is validated on an experimental model. The experimental structure is subjected to controlled loading scenarios to induce damage, thereby enabling a direct comparison between the numerical and experimental data. This comparison demonstrates the high sensitivity of AE techniques to damage progression, providing critical insights into the structural integrity of the bridge, representing a cost-effective solution for the long-term assessment of bridge health.