  Modeling parameters are essential to the fidelity of nonlinear models of
concrete structures subjected to earthquake ground motions, especially when
simulating seismic events strong enough to cause collapse. This paper addresses
two of the most significant barriers to improving nonlinear modeling provisions
in seismic evaluation standards using experimental data sets: identifying the
most likely mode of failure of structural components, and implementing data
fitting techniques capable of recognizing interdependencies between input
parameters and nonlinear relationships between input parameters and model
outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were
used to calibrate equations and black-box numerical models for nonlinear
modeling parameters (MP) a and b of reinforced concrete columns defined in the
ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of
failure. It was found that machine learning regression models and machine
learning black-boxes were more accurate than current provisions in the ACI
369.1/ASCE 41 Standards. Among the regression models, Regularized Linear
Regression was the most accurate for estimating MP a, and Polynomial Regression
was the most accurate for estimating MP b. The two black-box models evaluated,
namely the Gaussian Process Regression and the Neural Network (NN), provided
the most accurate estimates of MPs a and b. The NN model was the most accurate
machine learning tool of all evaluated. A multi-class classification tool from
the Scikit-learn machine learning library correctly identified column mode of
failure with 79% accuracy for rectangular columns and with 81% accuracy for
circular columns, a substantial improvement over the classification rules in
ASCE 41-13.
