Comparative Study of Modal Curvature and AI-Based Approaches for Vibration-Based Damage Detection in Structural Health Monitoring Systems of Prestressed Concrete Beams

Vibration-based damage detection methods are increasingly recognized as effective tools for monitoring the structural health of bridges. However, their reliability and applicability to various types of structural defects require further study, especially based on experimental tests, to correctly interpretate the results and compare the efficiency of different damage indexes. In the field of Structural Health Monitoring (SHM) by dynamic techniques, operational modal analysis (OMA) is of particular interest because only ambient signals are used, avoiding the service interruption of the infrastructures. However, the key issues of an efficient SHM are the possibility to have a quick alarm if an anomalous response is detected and the capability to localize the defect. Several methods can be applied for the anomaly detection considering machine learning, moving further than global modal parameters like the vibration frequency. Conversely for defect localization, local modal parameters, like modal curvature, can be efficient but also a different application of machine learning can be considered. In this paper, two approaches are compared for level 1 (detection) and 2 (localization) damage detection using acceleration measurements: the modal parameters and an Artificial Intelligence (AI)-based procedure using Variational Autoencoders (VAEs). The case study is a set of post-tensioned prestress concrete (PC) beams that represent a wide stock of existing bridges characterized by defects due to a reduction in the prestressing load, a lack of mortar in ducts, and corrosion of tendons. The results show that both methods can be effective, even if defects in PC beams are difficult to be detect with the dynamic response. Finally, the AI-based approach seems a promising solution because I allows for an earlier alarm, even with few sensors, while the modal curvature approach provides a better explanation of the identified anomaly, although it requires a greater number of sensors.