The present study contributes to the development of an automated system that leverages Deep Convolutional Neural Network-based approaches for the monitoring of prestressing wire breakage in concrete structures subjected to severe aging factors, such as bridges. Advanced methodologies for data acquisition and signal processing within the framework of Structural Health Monitoring (SHM) are explored, focusing on data augmentation techniques to address the critical issue of limited data availability. The work involved conducting controlled destructive tests on two prestressed concrete bridges in L'Aquila, Italy, prior to their planned demolition. A combination of accelerometers and acoustic emission sensors was utilized to capture vibration data during the controlled breakage of prestressing wires.