A quantitative identification approach for delamination in laminated composite beams using digital damage fingerprints (DDFs)

Digital damage fingerprints (DDFs) are a set of optimised and digitised characteristics of structural signatures, which are able to exactly and uniquely define a certain kind of structural healthy status. The DDF-based damage recognition technique includes the extraction of DDFs, assembly of damage parameters database (DPD) and subsequently inverse recognition in virtue of artificial intelligence. In this study, DDFs extracted from Lamb wave signals were employed to quantitatively assess delamination in carbon fibre-reinforced laminated beams. Characteristics of Lamb wave signals in the laminated beams were first evaluated, and DPD hosting DDFs for selected damage scenarios was constructed through numerical simulations, which was used to predict delamination in the composite beams with the aid of an artificial neural algorithm. The diagnostic results have demonstrated the excellent performance of DDF technique for quantitative damage identification.