Data-Driven Structural Health Monitoring in Laminated Composite Structures: Characterisation of Impact Damage

There is a high level of uncertainty for detecting damage, such as barely visible impact damage, hidden manufacturing defects, and subsurface cracks in laminated composites, which is a main limiting factor in wider use of these materials. This highlights the necessity of developing innovative structural health monitoring (SHM) strategies to meet the safety and reliability of current composite structures. In this research, a wide range of laminated composite specimens were designed, manufactured and tested under drop-weight impact with several impact energies to generate visual evidence of such impact events. The dataset was then used to train a user developed artificial intelligence (AI)-based algorithm to identify and predict damaged areas. The results showed that the developed algorithm could well identify the impact damage on both front and back faces of the specimens. The results obtained from the new AI-based platform were in good agreement with visual observations. The research highlights the importance of a high quality dataset in training the AI-based algorithms for visual SHM