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.

本文提出了 1) 一种新的混合分析技术，将数据驱动方法与基于物理的技术相结合，对地震荷载下的钢结构系统进行非线性分析，2) 两种基于组件的数据驱动模型（PI-SINDy 和DPI-SINDy）用于预测有或没有磁滞退化的钢地震熔断器的非线性磁滞响应。所提出的混合数据驱动和基于物理的模拟（HyDPS）技术为结构的地震分析提供了一种有效的方法，并有望解决与基于物理的数值模拟中固有的计算成本和建模不确定性相关的挑战。在该技术中，将数字建模的结构的易于理解的组件与使用本研究中开发的数据驱动模型之一模拟的结构的关键组件相结合。所提出的数据驱动模型是使用实验和数值滞后数据进行训练的。结果表明，这些数据驱动模型可以准确有效地预测钢结构构件在退化和未退化情况下的非线性迟滞响应。此外，通过在钢制屈曲约束支撑框架上执行响应历史分析，在存在噪声的情况下验证了由 PI-SINDy 模型支持的 HyDPS 技术的性能。