锈蚀预应力混凝土梁承载力及破坏模式研究

为提高锈蚀钢筋混凝土(RC)结构抗弯承载力评估精度，该文综合考虑锈蚀RC结构几何尺寸、钢筋截面积及力学性能、混凝土强度、粘结性能等因素，提出了基于改进粒子滤波(PF)算法的抗弯承载力模型参数更新及预测方法。通过生成大量的粒子以表征承载力退化过程中模型参数的不确定性，从选择不同建议密度函数的角度改进PF算法以解决传统PF算法中粒子退化的问题，分别采用PF、扩展粒子滤波(EPF)、无迹粒子滤波(UPF)算法对模型参数进行估计与更新，实现了锈蚀RC结构抗弯承载力的有效预测。结果表明：随着钢筋锈蚀率的增加，RC结构的抗弯承载力逐渐降低。基于改进PF算法的锈蚀RC结构抗弯承载力预测方法因考虑了模型参数更新使得预测结果更接近试验数据。基于EKF和UKF的改进PF算法可有效抑制粒子退化，其预测精度较PF算法更高；锈蚀RC结构抗弯承载力预测精度随着训练数据及粒子数的增加而提高。

Load bearing capacity and failure mode of corroded prestressed concrete beams

To improve the evaluation accuracy of flexural capacity of corroded reinforced concrete (RC) structures, a method for the model parameter updating and prediction of flexural capacity is proposed based on the improved particle filter (PF) algorithm. The proposed model comprehensively considers the geometric size of corroded RC structures, steel cross-sectional area and mechanical properties, concrete strength, bond performance and other factors. A large number of particles are generated to represent the uncertainty of model parameters during the degradation process of flexural capacity. The PF algorithm is improved from the perspective of selecting different proposal density functions to solve the problem of particle degradation in traditional PF algorithm. The PF, the extended particle filter (EPF) and the unscented particle filter (UPF) algorithm are employed to estimate and update the model parameters, which can effectively predict the flexural capacity of corroded RC structures. The results show that the flexural capacity of RC beams decreases gradually with the increase of steel corrosion loss. The prediction method of the flexural capacity of corroded RC structures based on the improved PF algorithm considers the updating of model parameters, which makes the prediction results closer to the reality. The improved PF algorithm based on EKF and UKF can effectively constrain the degradation of the particle, and the prediction accuracy is better than that of the PF algorithm. The prediction accuracy of the flexural capacity of corroded RC structures increases with the increase of training data and particle number.