A new framework is proposed for structural health monitoring based on nonlinear FE model updating using a batch Bayesian estimation approach.
The batch Bayesian approach leads to an extended ML estimation method to jointly identify the FE model parameters and measurement noise.
The parameter estimation uncertainties are quantified using the Cramer–Rao lower bound theorem by computing the Fisher Information matrix.
Two validation studies using numerically simulated data are provided to investigate the performance of the proposed framework.