Summary In this paper, a new sensitivity‐based model updating method is presented based on the changes of principal components (PCs) of frequency response function (FRF). Structural damage estimation, identification of damage location and severity, is conducted by an innovative sensitivity relation. The sensitivity relation is derived by incorporating PC analysis (PCA) data obtained from the incomplete measured structural responses in a mathematical formulation and is then solved by the least square method. In order to demonstrate the performance of the proposed method, it is applied to a truss and a frame model. The results prove the ability of the method as a robust damage detection algorithm in the presence of measurement and mass modeling errors. The comparative studies prove that the results obtained by the proposed sensitivity relation are more accurate than the results based on using FRF data directly.

No abstract

No abstract

No abstract

Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate the statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material non-linearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them.