  Backbone curves are used to characterize nonlinear responses of structural
elements by simplifying the cyclic force-deformation relationships. Accurate
modeling of cyclic behavior can be achieved with a reliable backbone curve
model. In this paper, a novel machine learning-based model is proposed to
predict the backbone curve of reinforced concrete shear (structural) walls
based on key wall design properties. Reported experimental responses of a
detailed test database consisting of 384 reinforced concrete shear walls under
cyclic loading were utilized to predict seven critical points to define the
backbone curves, namely: shear at cracking point; shear and displacement at
yielding point; and peak shear force and corresponding displacement; and
ultimate displacement and corresponding shear. The predictive models were
developed based on the Gaussian Process Regression method (GPR), which adopts a
non-parametric Bayesian approach. The ability of the proposed GPR-based model
to make accurate and robust estimations for the backbone curves was validated
based on unseen data using a hundred random sampling procedure. The prediction
accuracies (i.e., ratio of predicted/actual values) are close to 1.0, whereas
the coefficient of determination (R2) values range between 0.90-0.97 for all
backbone points. The proposed GPR-based backbone models are shown to reflect
cyclic behavior more accurately than the traditional methods, therefore, they
would serve the earthquake engineering community for better evaluation of the
seismic performance of existing buildings.
