Data Driven Frameworks For Hybrid Analysis Of Structures Under Seismic Loading

Numerical simulation and hybrid simulation are extensively used in earthquake engineering to evaluate the seismic response of structures under seismic loading. Despite the advances in computing power and the development of efficient integration algorithms in the past, numerical simulation techniques suffer from a high computational cost and the uncertainty associated with the definition of constitutive material models, boundary conditions, and mesh density, in particular in highly nonlinear, large or complex structures. On the other hand, the results of hybrid simulation can become biased when only one or limited number of potential critical components, seismic fuses, are physically tested due to laboratory or cost constraints. The recent progress in machine learning algorithms and applications in engineering has motivated novel and innovative simulation techniques achieved by leveraging data in various fields of engineering including seismic engineering where complexities arising from the stochastic nature of the phenomenon can be tackled by making use of available experimental and numerical data towards the development of more reliable simulation models and dynamic analysis frameworks. Furthermore, to better exploit the potential of data-driven models, such models can efficiently be incorporated into the physics-based and experimental techniques, leading to improved seismic response assessment methods. This M.Sc. thesis proposes two new hybrid analysis frameworks by integrating emerging data-driven techniques into the conventional structural response assessment techniques, namely numerical simulation and hybrid testing, to perform the nonlinear structural analysis under seismic loading. The first framework, referred to as the hybrid data-driven and physics-based simulation (HyDPS) technique, combines the well-understood components of the structure modeled numerically with the critical components of the structure, e.g., seismic fuses, simulated using the proposed data-driven PI-SINDy model. The data-driven model is developed for steel buckling-restrained braces based on experimental data to mathematically estimate the underlying relationship between displacement history and restoring force. The second framework incorporates the data-driven model into the conventional seismic hybrid simulation framework where the experimental test data of one of the critical components (physical twin), e.g., steel buckling-restrained brace, produced during hybrid simulation can be used in real-time to predict the nonlinear cyclic response of the other critical components of the system (digital twins) that are not physically tested. This framework features a novel multi-element seismic hybrid simulation technique achieved by recursively updating the force-deformation response of the digital twin. The performance of the proposed data-driven hybrid analysis frameworks is verified using past experimental test data and nonlinear response history analyses performed under representative earthquake ground motion accelerations. The results reveal that integrating data-driven techniques into conventional seismic analysis methods, namely numerical simulation and hybrid simulation, yields a more efficient seismic simulation tool that can be used to examine the seismic response of structural systems.