Structural Failure Mode Prediction in Laterally Loaded Reinforced Concrete Columns Using Machine Learning

Research has demonstrated the critical role of reinforced concrete (RC) columns in major built infrastructure systems such as bridges and high-rise buildings. To build upon this, this study aims to demonstrate the effectiveness of the support vector machine (SVM) learning technique in predicting the potential failure mode of laterally loaded spiral RC columns. The Pacific Earthquake Engineering Research Center’s laboratory test data of 159 spiral RC columns was used for this data-driven study, and the SVM model adopted the radial basis function kernel and optimized values of the hyperparameters cost and gamma to develop a robust SVM classification model. The model has an accuracy of 94.59% and a Cohen’s kappa coefficient of 0.92. Overall, this study has successfully demonstrated machine learning tools’ effectiveness and usability in data-driven engineering problem-solving and would contribute immensely to the domain knowledge on using technological tools to solve society’s most pressing challenges.