Hybrid data-driven and physics-based simulation technique for seismic analysis of steel structural systems.

This paper proposes 1) a new hybrid analysis technique by integrating a data-driven method with a physics-based technique to perform nonlinear analysis of steel structural systems under seismic loading, 2) two component-based data-driven models (PI-SINDy and DPI-SINDy) for predicting the nonlinear hysteretic response of steel seismic fuses with and without hysteretic degradation. The proposed hybrid data-driven and physics-based simulation (HyDPS) technique offers an efficient approach for seismic analysis of structures and is expected to address the challenges associated with computational cost and modeling uncertainties inherent in physics-based numerical simulations. In this technique, the well-understood components of the structure modeled numerically are combined with the critical components of the structure simulated using one of the data-driven models developed in this study. The proposed data-driven models were trained using experimental and numerical hysteresis data. The results show that these data-driven models can accurately and efficiently predict the nonlinear hysteretic response of steel structural components with and without degradation. Furthermore, the performance of the HyDPS technique powered by the PI-SINDy model is verified in the presence of noise using response history analyses performed on a steel buckling-restrained braced frame. • Proposed a hybrid seismic analysis technique for steel structures combining data-driven and physics-based models. • Developed two sparse regression-based data-driven models for simulating steel structures with/without degradation. • Utilized a dimensionality reduction algorithm to enhance the efficiency of the data-driven model with degradation. • Validated the data-driven models and hybrid technique with experimental and synthetic numerical data. [ABSTRACT FROM AUTHOR]