基于贝叶斯理论的钢筋再生混凝土梁受剪承载力预测模型

Shear capacity is one of the important performance indicators for the design and repair of reinforced recycled aggregate concrete (RAC) structures, so it is of great scientific and practical significance to develop its calculation method. At present stage, due to the shortage of test results and the high variability for the RAC's mechanical property, most specifications still applies calculation formulas of conventional concrete beam to estimate that capacity by simply reducing the RAC's strength, which usually has problems such as low accuracy and poor stability. In view of this, a shear test database containing 206 reinforced RAC beams without stirrups was first established to evaluate the accuracy and reliability of existing specifications and empirical formulas. Then, the ACI318-2014 code with good prediction effect and the formula suggested by scholar Zsutty were selected as the prior model, and the prior model and test information were statistically inferred based on Bayesian statistical theory to build the probability model for calculating shear capacity of reinforced RAC beams without stirrups. Finally, Bayesian parameter culling process was adopted to eliminate the secondary influential factors, and new predictive expressions were acquired. The results showed that the Bayesian method fully integrated the completeness of the prior model and the accuracy of a large number of test data, and could predict the reinforced RAC beam's shear strength more accurately.