中文摘要：

为保证子结构拟动力试验中数值子结构的可靠性,模型参数在线识别与更新方法逐渐受到关注。对于钢筋混凝土结构,当采用纤维模型建立数值子结构时,混凝土材料本构模型参数的选择具有较大不确定性。因此,该文提出了基于隐性卡尔曼滤波器在线识别混凝土材料本构模型参数的方法。首先,对材料本构模型参数进行分类,定义了本构参数与非本构参数,提出了约束混凝土与非约束混凝土的一致本构方程。然后,针对观测量为混凝土应力的情况进行数值仿真分析,验证了此方法的可行性。最后,通过修改Open Sees源代码,实现了此方法在观测量为构件恢复力情况下的应用。研究结果表明该文提出的方法具有较好的稳定性与较高的精度,从而在很大程度上提高了数值模型的可靠性。

英文摘要：

In order to ensure the reliability and to improve the accuracy of a numerical substructure in a substructure pseudo dynamic test, parameter identification and modal updating have been proposed and recently received more attention. This paper proposes a method based on unscented Kalman filter for online identifying the parameters of a concrete constitutive model to solve the problem that the parameters are difficult to determine when establishing the numerical substructure with a fiber model. Firstly, the constitutive equations of confined and unconfined concrete are unified by defining constitutive parameters and non-constitutive parameters. Then, on the condition that the stress of concrete could be measured, the proposed method is validated through numerical simulation. Finally, some parts of the OpenSees source codes are modified to implement this identification method on an actual situation that the restoring forces of the experimental substructures could be measured. The results show that the method possesses good stability and accuracy. Thereby, it largely improved the reliability of the numerical model.