Characterization and Detection of Damage in Heterogeneous Systems

Global damage detection techniques based on natural frequencies and mode shapes have proven ineffectual in detecting damage smaller than 10 percent of the surface area of the structure and near-surface damage. A damage index involving a convolution product of the difference in the layer-wise in-plane modal strains between a reference and damaged states of a structure was developed to qualify damage. This is an inverse multi-modal problem, associated with a large number of local optima. To further complicate this problem, both continuous and discrete design variables need to be adopted. A tailored genetic algorithm has been used in conjunction with an artificial back-propagation neural network, which is used as a function approximator with satisfactory accuracy to determine the damage index response of the delaminated laminate with various de-lamination patterns.