Detection of local matrix cracks in composite beam using modal data and modular radial basis neural networks

Abstract

Dynamic characteristics such as natural frequencies and mode shapes are used to identify the location of damage and the damage level in a laminated composite beam with localized matrix cracks. Such cracks can be the result of low velocity impact damage and are hard to detect visually. Natural frequencies are used in conjunction with modular radial basis function neural networks for damage detection. The mode shapes are utilized to obtain a damage indicator called curvature damage factor (CDF). A matrix crack based damage model is integrated with a beam finite element model to simulate the damaged composite beam structure. In the matrix crack model, the stiffness of the beam is degraded by a reduction in A, B and D matrices to simulate the damage and the damage level is represented by matrix crack density. It is found that the combination of modular radial basis neural networks with natural frequencies and CDF can be used as robust damage detection tools for localized matrix cracks in composite beams.