  Numerical modeling of different structural materials that have highly
nonlinear behaviors has always been a challenging problem in engineering
disciplines. Experimental data is commonly used to characterize this behavior.
This study aims to improve the modeling capabilities by using state of the art
Machine Learning techniques, and attempts to answer several scientific
questions: (i) Which ML algorithm is capable and is more efficient to learn
such a complex and nonlinear problem? (ii) Is it possible to artificially
reproduce structural brace seismic behavior that can represent real physics?
(iii) How can our findings be extended to the different engineering problems
that are driven by similar nonlinear dynamics? To answer these questions, the
presented methods are validated by using experimental brace data. The paper
shows that after proper data preparation, the long-short term memory (LSTM)
method is highly capable of capturing the nonlinear behavior of braces.
Additionally, the effects of tuning the hyperparameters on the models, such as
layer numbers, neuron numbers, and the activation functions, are presented.
Finally, the ability to learn nonlinear dynamics by using deep neural network
algorithms and their advantages are briefly discussed.
