This discrepancy is due to a number of factors, ranging from the noise in measurement, normal variation of the structure, to the error in the finite element model itself.
The essence of Bayesian inference is to establish a probabilistic model to correct the prior beliefs based on the evidences.
It starts from specifying the model parameters with prior information in the form of PDF, which may be viewed as imposing soft physical constraints to enable a unique and stable solution.
This can reveal the underlying properties of structures under the inevitable uncertainties and variations .
Several types of probabilistic approaches have been explored for this purpose.
The result shows the effectiveness of the proposed method and certain significance in parameter selection for model updating.
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.The issue is further compounded by the uncertainties and normal variations mentioned above [8–10].Ideally, model updating should be conducted in the probabilistic sense, i.e., treating model parameters to be updated as random variables with means and variances.Soize presented a nonparametric probabilistic approach based on random matrix theory to model the structural uncertainties and estimate the dispersion parameters . developed a perturbation scheme to analyze the statistical moments of updated parameters from measured variability in structural modal responses .It is worth noting that the Bayesian inference-type of methods has recently attracted significant attention due to some intrinsic advantages [14–16].Then, by introducing measured response data, the assumed prior PDF is updated to the so-called posterior PDF that will then be analyzed to yield the optimal model parameters.