Structural Joint Damage Assessment using Neural Networks

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Structural damage is used to be modeled through reductions in the stiffness of structural elements for the purpose of damage estimation of structural system. In this study, the concept of joint damage is employed for more realistic damage assessment of a steel structure. The joint damage is estimated damage based on the mode shape informations using neural networks. The beam-to-column connection in a steel frame structure is represented by a rotational spring at the fixed end of a beam element. The severity of joint damage is defined as the reduction ratio of the connection stiffness with respect to the value of the intact joint. The concept of the substructural identification is used for the localized damage assessment in a large structure. The feasibility of the proposed method is examined using an example with simulated data. It has been found that the joint damages can be reasonably estimated for the case with the measurements of the mode vectors subjected to noise.