Predicting the Plastic Rotational Capacity of Reinforced Concrete Elements Using Machine Learning

Abstract This study investigates the use of machine learning algorithms to predict the plastic rotational capacity of reinforced concrete elements, a critical factor in assessing the response and vulnerability of structures under seismic loads. Traditional mathematical models developed to evaluate the rotational capacity of concrete elements are insufficient for accurately capturing the complex nonlinear behavior of structures under seismic loads. Machine learning offers a promising alternative, given its ability to model nonlinear relationships and improve predictive accuracy based on diverse datasets. The study compares the performance of six machine learning algorithms - Linear Regression (LR), Support Vector Machine for Regression (SVR), Least Squares Support Vector Machine (LS-SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) - using datasets of reinforced concrete columns and walls. Results reveal that SVR and XGBoost provide the most accurate predictions for columns, with R² values up to 91%, while XGBoost performs best for walls with a maximum R² of 78%. However, the accuracy of these models is limited by the availability and diversity of the datasets, particularly for elements prone to shear failure. This research underscores the potential of machine learning in seismic design and structural assessment, though it also highlights the need for more comprehensive data to enhance model accuracy.