Finite element model updating of a small steel frame using neural networks

This paper presents an experimental and analytical dynamic study of a small-scale steel frame. The experimental model was physically built and dynamically tested on a shaking table in a series of different configurations obtained from the original one by changing the mass and by causing structural damage. Finite element modelling and parameterization with physical meaning is iteratively tried for the original undamaged configuration. The finite element model is updated through a neural network, the natural frequencies of the model being the net input. The updating process is made more accurate and robust by using a regressive procedure, which constitutes an original contribution of this work. A novel simplified analytical model has been developed to evaluate the reduction of bending stiffness of the elements due to damage. The experimental results of the rest of the configurations have been used to validate both the updated finite element model and the analytical one. The statistical properties of the identified modal data are evaluated. From these, the statistical properties and a confidence interval for the estimated model parameters are obtained by using the Latin Hypercube sampling technique. The results obtained are successful: the updated model accurately reproduces the low modes identified experimentally for all configurations, and the statistical study of the transmission of errors yields a narrow confidence interval for all the identified parameters