TOPSIS based multi-fidelity Co-Kriging for multiple response prediction of structures with uncertainties through real-time hybrid simulation

Energy dissipation devices in vibration control often present challenges for accurate modeling and uncertainty quantification through computational simulation. Simplified numerical models of these devices might not realistically represent their behavior under earthquakes thus lead to errors in response prediction and uncertainty quantification. This study further explores the integration of Co-Kriging meta-modeling and real-time hybrid simulation (RTHS) for global response prediction of multi-degree-of-freedom systems under the presence of structural uncertainties. RTHS in laboratory is taken as high-fidelity (HF) model while computational simulation with approximate modeling is used as low-fidelity (LF) model. Multi-fidelity modeling is integrated through Co-Kriging to render accurate response prediction over the entire sample space of uncertainty. An entropy-based sequential sampling is integrated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to sequentially determine new sampling points for HF and LF simulation. The proposed TOPSIS based multi-fidelity Co-Kriging approach is experimentally evaluated through RTHS of a two-degree-of-freedom structure with self-centering viscous dampers. Accuracy of Co-Kriging prediction are further evaluated through validation tests. It is demonstrated that TOPSIS can effectively reduce the number of RTHS tests in laboratory required by multi-fidelity Co-Kriging to achieve better prediction accuracy. The study presents an innovative and effective way to apply RTHS for efficient uncertainty quantification of multiple response quantities.