Real-time hybrid simulation incorporating machine learning for deep-water bridges subjected to seismic ground motion with fluid-structure dynamic interaction

Abstract

The investigation of fluid-structure dynamic interaction (FSI) of deep-water structures subjected to seismic events with underwater shaking table testing method has been restricted by similarity problems and limited experimental capabilities. To address this issue, the authors previously proposed a real-time hybrid simulation (RTHS) method for FSI under seismic loading. In this study, the proposed method is applied to a deep-water bridge to demonstrate the low-cost and high-efficiency features of the proposed method. A total of 100 sets of experimental samples were analyzed, providing details and statistical information of the structural responses with FSI effects during seismic events. In the proposed RTHS application, a machine learning surrogate model was established for real-time calculation of the complex numerical substructure, and data exchange for complex physical-numerical boundaries. Additionally, a solution for RTHS when complex physical substructure boundary conditions can only be partially satisfied has been proposed, further contributing to the versatility of the study. Through the utilization of such advanced computational methods, the authors were able to explore the intricate coupled effect between fluid and structure in a deep-water environment. Such efforts hold the promise of enhancing our understanding of this complex phenomenon and may ultimately inform the design and construction of more resilient structures in the future.