  Dynamic response evaluation in structural engineering is the process of
determining the response of a structure, such as member forces, node
displacements, etc when subjected to dynamic loads such as earthquakes, wind,
or impact. This is an important aspect of structural analysis, as it enables
engineers to assess structural performance under extreme loading conditions and
make informed decisions about the design and safety of the structure.
Conventional methods for dynamic response evaluation involve numerical
simulations using finite element analysis (FEA), where the structure is modeled
using finite elements, and the equations of motion are solved numerically.
Although effective, this approach can be computationally intensive and may not
be suitable for real-time applications. To address these limitations, recent
advancements in machine learning, specifically artificial neural networks, have
been applied to dynamic response evaluation in structural engineering. These
techniques leverage large data sets and sophisticated algorithms to learn the
complex relationship between inputs and outputs, making them ideal for such
problems. In this paper, a novel approach is proposed for evaluating the
dynamic response of multi-degree-of-freedom (MDOF) systems using
physics-informed recurrent neural networks. The focus of this paper is to
evaluate the seismic (earthquake) response of nonlinear structures. The
predicted response will be compared to state-of-the-art methods such as FEA to
assess the efficacy of the physics-informed RNN model.
