Machine learning-based prediction of shear strength of steel reinforced concrete columns subjected to axial compressive load and seismic lateral load

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The shear strength of steel reinforced concrete (SRC) columns is a crucial basis in their seismic design and evaluation of structural performance. Most of the existing research predicts the lateral load carrying capacity based on regression equations, but lacks accuracy. To this end, in this study, Gaussian process regression (GPR), Least Squares Boosting (LSBoosting), Support Vector Regression (SVR), Feedforward Neural Network (FNN) and other machine learning (ML) algorithms were employed to develop the shear strength model of SRC columns subjected to axial compressive load and seismic lateral load. A large experimental database with 395 samples was established to train 11 input features. The original database was divided into 5 datasets based on the mixed, flexural, flexural-shear, shear, and bond failure modes. The failure mechanism factors were introduced for better evaluation. Specifically, an effective data splitting strategy-bootstrapping was used to train the models, and Bayesian optimization was attempted to improve the prediction accuracy. Results demonstrate that GPR has the highest prediction accuracy with the most failure modes, and the factors affecting the shear strength are explained reasonably by correlation analysis and Partial Dependence Plot. Through comprehensive comparison, the GPR model developed in this paper is advanced in predicting the shear strength of SRC columns.