Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

Abstract

The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.

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“…Congregating numerical and experimental results, they concluded that the collapse likelihood of a substandard RC under design earthquake increases from 4.20% to 29.20%. Phan et al [10] integrated regression and classification schemes to predict shear strength and failure modes in rectangular RC columns. Using 541 experimental datasets, they concluded that the K-nearest neighbor (KNN) outperformed any other classifier or regressor.…”

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