Natural Hazards Research Summit 2022: Multi-Directional Cyber-Physical Experimental Testing to Establish Multi-natural Hazard Resiliency of Structural Systems and Building Content Hazard Mitigation

Protecting both the essential building contents and the structural system—as well as facilitating and accelerating the post-event functionality of business operations—is a major concern during natural hazards. Floor isolation systems (FIS) with rolling pendulum bearings along with nonlinear fluid viscous dampers (NFVD) have been proposed to mitigate damage and enhance the resiliency of non-structural and structural systems, respectively. These devices are designed to decrease vibrations under dynamic loading conditions. In this poster, we introduce research using tridimensional nonlinear cyber-physical experimental testing (i.e., real-time hybrid simulations) to validate the performance of these response modification devices placed in structural systems under wind and earthquake loading conditions. The effects of soil-structure-foundation and fluid-structure interactions were also accounted for. The novelty of the project is the use of multi-directional large-scale real-time hybrid simulations of complex nonlinear systems under wind and earthquake demands to combine experimental structural modification passive devices with analytical multi-story buildings considering soil-foundation interaction via neural network. Results show that the FIS and NFVD can significantly reduce the demand on non-structural and structural systems of buildings subjected to natural hazards whose response can be also significantly affected by soil-foundation-structure interaction. A product of this research is the data (which is linked in Related Works), which can be used to compare with new studies using the same experimental techniques and structural modification devices or with alternative approaches. Researchers interested in multi-natural hazards resilience and mitigation, state-of-the-art structural experimental techniques, and the use of machine learning as a tool to improve modeling efficiency will benefit from its results. Also, companies dedicated to the commercial development of structural response modification devices, as well as policymakers working or with interest in economic and social resilience.