Hybrid mechanical and data-driven probabilistic model for shear strength of RC beam-column joints

Abstract

In order to overcome the limitations of traditional data-driven or mechanics-based methods, a novel hybrid mechanical and data-driven probabilistic model for the shear strength of reinforced concrete (RC) beam-column joints was proposed based on the modified Gaussian process (GP). A new mean function for the GP was developed first by considering the mechanical mechanisms of joint shear strength to improve the prediction accuracy and generalization ability. Then a hybrid mechanical and data-driven probabilistic model (HMDPM) for the joint shear strength was established based on the modified GP. Moreover, the hyper-parameters of the prior mean and covariance functions of the HMDPM were jointly optimized by maximum likelihood estimation (MLE). Finally, the HMDPM was compared with traditional GPs and mechanics-based methods by using 137 sets of test data. Comparisons show that the proposed model is less sensitive to training data sizes and has superior extrapolation ability for out-of-distribution predictions as compared to traditional GPs. In addition, the proposed model not only has satisfactory prediction performance, but can also reasonably describe the probabilistic characteristics of joint shear strength compared with traditional mechanics-based methods. Comparisons demonstrate that the proposed model has high prediction accuracy, robustness and generalizability, which provides an alternative approach to predicting joint shear strength.