In this research, a sensitivity approach to finite element model updating is used to determine stiffness reduction factors from measured structural response. The used method causes a set of nonlinear ill-conditioned equations that need to be linearized and regularized in order to find the solution. A new approach to solve the problem is presented using variable regularization parameter. Utilization of variable regularization parameter eliminates dependency on the number of iterations and prevents the loss of regularization effect due to iterations. A new stopping criteria is used which is based on the difference between mean and variance of last iterations. Furthermore the results show that using wavelet transform to update the model yields better results than modal parameters. Expedient performance of the proposed method is shown through a numerical simulation.
This paper presents new ground-motion prediction equations for three distinct seismic regions of Iran via updating the previous global model using observed data for each region by means of Bayesian updating. The Bayesian theory has the advantage that it results in more accurate results even in situations when little data is available. This leads the way for updating global models to obtain new local models for seismotectonic regions with little available data like Iran. The proposed updated model was compared against currently available models for Iran and the results reveal the overall stability and quality performance of the proposed model.
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