Machine learning tools to improve nonlinear modeling parameters of RC columns

TL;DR

This study employs machine learning techniques to enhance the accuracy of nonlinear modeling parameters for reinforced concrete columns, improving failure mode prediction and parameter estimation over existing standards.

Contribution

It introduces machine learning models that outperform current seismic standards in estimating nonlinear parameters and failure modes of RC columns.

Findings

Machine learning models are more accurate than current standards.

Neural Networks provided the best estimates among evaluated models.

Failure mode classification accuracy improved to over 79%.

Abstract

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.