Damage Detection and Structural Health Monitoring of ST-37 Plate Using Smart Materials and Signal Processing by Artificial Neural Networks

Structural health monitoring (SHM) systems operate online and test different materials using ultrasonic guided waves and piezoelectric smart materials. These systems are permanently installed on the structures and display information on the monitor screen. The user informs the engineers of the existing damage after observing the signal loss which appears after damage is caused. In this paper, monitoring is done for plate shaped structures made of ST-37 steel. After conducting the experimental tests, the stored signals by the multi-layer artificial neural network algorithm are processed and the damage caused in the plate is detected. By analyzing the graphs, it becomes clear that after causing damage the signal amplitude decreases. In the experimental test, two piezoelectric discs are used on a steel plate which has been installed using a strong adhesive. Using a strong adhesive improves wave propagation in the structure. Developing innovative testing methods for the SHM system has caused better control in structures after assembly.