Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework

Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics 61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.