Displacement and strain data-driven damage detection in multi-component and heterogeneous composite structures

This work introduces the use of convolutional neural network (CNN) in combination with advanced structural theories for the damage detection of multi-component and composite structures. Well-established component-wise (CW) models based on the Carrera Unified Formulation (CUF) are developed first to demonstrate the effect of localized damages on the mechanical performance of thin-walled beams and laminates. Finite element Monte Carlo simulations of randomly damaged structures are then used to generate a large database of full-field displacement and strain images. These images are lately feed into a dedicated CNN for training purpose and for the prediction of location and intensity of structural damages occurring in unseen scenarios. The results demonstrate the validity of the present approach and suggest the importance of adopting opportune structural models to carry out localized damage detection by image-driven AI. Overall, the research provides good confidence for future investigation and experimental testing.