Bayesian Model Updating for a Long‐Span Bridge Considering Modeling Errors

Finite element (FE) model updating methods are widely used in the field of structural health monitoring as well as performance evaluation. Whenever modeling is involved, whether it is a theoretical model or a FE model, the modeling error is a factor that will not be eliminated and has a significant impact on the model update results. It usually leads to residual errors in the update results, and large discrepancies between the determined model parameters and the corresponding physical values in the actual situation. In this paper, a new two‐stage Bayesian updating theory is developed to study the model updating problem considering modeling errors. The model updating parameters are explicitly divided into traditional model parameters and modeling error parameters, and these two parameters are updated separately. Meanwhile, the modeling error is also divided into measurement error and FE error, whose manifestations in the objective function are discussed separately. The FE computational errors are divided into frequency‐dependent modeling errors and modal computational errors, and a simulated numerical simulation model is used to verify the feasibility of model updating and the effects of model updating results after considering the modeling errors. Subsequently, to verify the practical application of the method, for a real bridge, the modified parameters and modes are screened to avoid the situation of too many parameters of the objective function, and a suitable optimization algorithm is selected during the calculation process so as to improve the computational efficiency. The results show that the algorithm considering the modeling error will make the bridge update more accurate and effectively improve the accuracy of the model update.