A simple and effective Measurement-Changes-Correction strategy for damage identification with aleatoric and epistemic model errors

The model errors are often inevitably encountered when a practical structure is analyzed by a mathematical baseline model, and such model errors would have undesired effects on damage identification. Aiming to alleviate the effect of the model errors, a new Measurement-Changes-Correction strategy that simply uses measurement changes to correct the measured data is developed in this article. To this end, the performance of the Measurement-Changes-Correction strategy under the aleatoric and epistemic model errors is systematically analyzed. The analysis is proceeded along with comparison to the conventional two-step strategy where the intact damage parameters are first updated from the measured data and then, damage identification is conducted upon the updated parameters. Thereafter, it is found that (1) the Measurement-Changes-Correction strategy performs as well as the two-step strategy under small aleatoric and epistemic model errors, (2) the Measurement-Changes-Correction strategy can still work under large scaling model parameter errors, but the two-step strategy may not, and (3) the Measurement-Changes-Correction strategy only requires correction of the measured data by the measurement changes so that the computation cost by the Measurement-Changes-Correction strategy is almost half of that by the two-step strategy. Numerical examples and International Association for Structural Control–American Society of Civil Engineers benchmark problems are studied to verify the performance of the proposed Measurement-Changes-Correction strategy.