Cover page Title: Damage Detection in FRP Laminated Beams Using Neural Networks Authors: Melin Sahin

This paper presents a technique to predict the severity and the location of the damage in beam-like composite laminates by using modal parameters as input for an artificial neural network. A laminated cantilever beam is modelled using ANSYS 5.6© finite element software. Normal mode dynamic analyses have been performed for the first three natural modes of intact and damaged beams to find the modal parameters. Damage has been modelled as a local reduction in stiffness of the selected elements in the finite element model. Considering various stiffness reductions at different locations along the beam, a variety of damage scenarios have been created. Natural frequencies and corresponding displacement mode shapes have been obtained from finite element analyses and the curvature mode shapes of the beam have been calculated from the normalised displacement mode shapes by using finite difference method. Following the sensitivity analyses aimed at finding the necessary parameters for the damage detection, different input-output sets have been introduced to various artificial neural networks for training. Finally, a trained feed-forward backpropagation artificial neural network is tested using new damage cases and checks are made for severity and location prediction of the damage.