  Hybrid simulation (HS) is a widely used structural testing method that
combines a computational substructure with a numerical model for
well-understood components and an experimental substructure for other parts of
the structure that are physically tested. One challenge for fast HS or
real-time HS (RTHS) is associated with the analytical substructures of
relatively complex structures, which could have large number of degrees of
freedoms (DOFs), for instance. These large DOFs computations could be hard to
perform in real-time, even with the all current hardware capacities. In this
study, a metamodeling technique is proposed to represent the structural dynamic
behavior of the analytical substructure. A preliminary study is conducted where
a one-bay one-story concentrically braced frame (CBF) is tested under
earthquake loading by using a compact HS setup at the University of Nevada,
Reno. The experimental setup allows for using a small-scale brace as the
experimental substructure combined with a steel frame at the prototype
full-scale for the analytical substructure. Two different machine learning
algorithms are evaluated to provide a valid and useful metamodeling solution
for analytical substructure. The metamodels are trained with the available data
that is obtained from the pure analytical solution of the prototype steel
frame. The two algorithms used for developing the metamodels are: (1) linear
regression (LR) model, and (2) basic recurrent neural network (RNN). The
metamodels are first validated against the pure analytical response of the
structure. Next, RTHS experiments are conducted by using metamodels. RTHS test
results using both LR and RNN models are evaluated, and the advantages and
disadvantages of these models are discussed.
