Adaptive neural networks for model updating of structures

Structural health monitoring is becoming increasingly important, not only in the prevention of catastrophic failures, but also in improving maintainability of aerospace, civil, and mechanical infrastructure.An improved understanding of the capacity and performance of a structure within its serviceability limit states is a critical need and most often implemented by means of structural identification (STI) [1].Based on the measured modal data, the presented method was verified.The results show that this model updating technique can reflect the true dynamic characters of the shell structure better.

Secondly, the value of stiffness reduction factor () of it was suggested to be 0.5 based on referenced literatures.The influences of the dowel pin and pretightening force were ignored.The ; the elastic modulus of the joint element was determined from the obtained curve.Both the substructure method and radial basis function neural network (RBF) were introduced into finite element model updating of single pylon cable-stayed bridges [4], which simplified the process of model updating and greatly improved the updating efficiency.A stepwise model updating method based on substructures and ANN was presented by He et al. A single-layer reticulated dome was first divided into multilayer substructures, and model updating was then done step by step according to appropriate damage factors.

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