A finite element model updating method based on the trust region and adaptive surrogate model

The traditional finite element (FE) model updating method based on a static surrogate model often suffers from high calculation costs and low updating accuracy. To address these problems, an FE model updating method based on the trust region (TR) and an adaptive surrogate model is proposed in this study, and the inverse identification of model parameters is realized through sampling. First, an average sensitivity method is introduced to select the parameters to be updated as the inputs of the surrogate model. Second, the residuals between the calculated re-sponses and the experimental responses are taken as the outputs of the surrogate model, and the objective function measuring the misfit between the FE model and the actual structure is con-structed. The FE model updating problem is directly converted into a surrogate model optimi-zation problem. Then, during the iterations, adaptive sampling is carried out through the multi -prediction value (MPV) infill criterion, and the TR method is adopted to restrict the sampling space and improve the prediction accuracy of the surrogate model while reducing the calculation efforts of FE model updating to guide the surrogate model to find the global optimal solution. Finally, the proposed method is verified using a simply supported beam and steel truss bridge test. The results reveal that under a limited number of calls to the FE model, the proposed method can not only obtain the updated parameters with physical meaning but also provide high updated accuracy and efficiency.