Mechanical- and Data-Driven Model for Probabilistic Shear Strength of Interior Beam-Column Joints
Bo Yu, Zecheng Yu, Hao Cheng, and Bing Li
Appears on pages(s):
calibration; confidence interval; data-driven; interior beamcolumn joints; mechanical-driven; probabilistic shear strength
A major limitation of traditional joint shear strength models is the fact that they cannot consider the shear-resistance mechanisms and various uncertainties comprehensively. To overcome this limitation, a hybrid mechanical- and data-driven model for probabilistic shear strength of reinforced concrete (RC) interior beam-column joints was proposed. A simplified deterministic mechanical model for shear strength of RC interior beam-column joints was derived first. Then, a probabilistic shear strength model for RC interior beam-column joints was developed using a data-driven approachconsidering both aleatory and epistemic uncertainties. Moreover, the posterior distributions of probabilistic model parameters were updated by means of the Bayesian theory and the Markov chain Monte Carlo method. Finally, the proposed probabilistic shear strength model was verified by using available experimental data and seven typical shear strength models. Comparisons show that the proposed model not only provides reasonable prediction accuracy but also describes the probability distribution of RC interior beam-column joints realistically. Furthermore, the proposed model is useful for the calibration of accuracy and applicability of available deterministic shear strength models, which provide probabilistic calibration methods based on the confidence interval and probability density function.