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In seismic regions, structures along the coast may be exposed to earthquake and tsunami loading during their service life. During the 2011 Great East Japan Earthquake, many structures survived the earthquake but failed due to the subsequent tsunami loading. This research aims to generate data about the effects of tsunami waves on coastal structures, however, conventional approaches have limitations when simulating structures interacting with hydrodynamics. Computational methods require experimental validation, but scaled experimental methods may not represent full-scale prototype response because of the unique similitude law governing the hydrodynamics versus the structural dynamics. Real-time hybrid simulation (RTHS) can alleviate the similitude limitations by partitioning the system subjected to structural- and hydrodynamics into physical and numerical sub-assemblies. The sub-assemblies interact through actuators and sensors in real time, which enables the application of individually applied similitude laws to each sub-assembly. Here, physical solitary waves and a very stiff cylindrical physical specimen were coupled with a numerical single degree-of-freedom (SDOF) oscillator via RTHS. In the NHERI Large Wave Flume at Oregon State University, breaking and broken solitary waves excited the physical specimen, whose natural period was then numerically manipulated. Results showed that the effects of wave-structure interaction depend on the duration of the wave loading and natural period of the SDOF system. 

The demand for high-performance computing resources has led to a paradigm shift towards massive parallelism using graphics processing units (GPUs) in many scientific disciplines, including machine learning, robotics, quantum chemistry, molecular dynamics, and computational fluid dynamics. In earthquake engineering, artificial intelligence and data-driven methods have gained increasing attention for leveraging GPU-computing for seismic analysis and evaluation for structures and regions. However, in finite-element analysis (FEA) applications for civil structures, the progress in GPU-accelerated simulations has been slower due to the unique challenges of porting structural dynamic analysis to the GPU, including the reliance on different element formulations, nonlinearities, coupled equations of motion, implicit integration schemes, and direct solvers. This research discusses these challenges and potential solutions to fully accelerate the dynamic analysis of civil structural problems. To demonstrate the feasibility of a fully GPU-accelerated FEA framework, a pilot GPU-based program was built for linear-elastic dynamic analyses. In the proposed implementation, the assembly, solver, and response update tasks of FEA were ported to the GPU, while the central-processing unit (CPU) instructed the GPU on how to perform the corresponding computations and off-loaded the simulated response upon completion of the analysis. Since GPU computing is massively parallel, the GPU platform can operate simultaneously on each node and element in the model at once. As a result, finer mesh discretization in FEA will not significantly increase run time on the GPU for the assembly and response update stages. Work remains to refine the program for nonlinear dynamic analysis.