考虑强度和刚度退化及捏拢效应的钢筋混凝土桥墩滞回模型及其参数识别

Hysteresis model and parameter identification of RC bridge piers considering strength and stiffness degradation and pinching effect

为了有效地模拟强震作用下桥梁结构的非线性地震反应,需要确定钢筋混凝土(RC)桥墩非线性力-位移滞回关系。基于Bouc-Wen模型,提出了一种考虑RC桥墩强度和刚度退化以及捏拢效应的改进非线性滞回模型,确定了影响该滞回模型的控制参数,并在Matlab/Simulink里实现了对RC桥墩各种破坏模式非线性滞回性能预测。同时,通过对1/3比尺的RC桥墩分别在压弯、扭转和弯剪作用下的往复荷载试验研究,试验得到的力-位移关系滞回曲线与所提的改进滞回模型预测结果吻合较好,验证了改进模型的正确性和有效性。并采用汶川地震波对绵州市回澜立交桥匝道桥的RC桥墩的滞后性能进行了数值模拟,分析结果和桥墩的震害一致,证实了采用本文提出的改进滞回模型可以较准确地预测RC桥墩在强震作用下的非线性滞后性能。在此基础上,运用无迹卡尔曼(UKF)方法对Bouc-Wen模型进行参数估计,模拟结果和误差分析表明,该方法能够精确的估计出改进的Bouc-Wen模型的参数。

In order to effectively simulate the nonlinear hysteresis behavior of reinforced concrete （RC） bridge piers under strong earthquake excitation, an improved nonlinear hysteresis model for RC bridge piers was developed and its controlling parameters were determined considering stiffness and strength degradation and pinching effect based on classical Bouc-Wen model. The improved model can be carried out to predict the nonlinear hysteresis behavior of RC bridge piers under various failure modes using MATLAB/ Simulink program. Cyclic tests of different failure mode bridge column specimens were performed under constant axial load with lateral bending. The results did show that force-displacement relationship curves of bridge column specimens derived from theoretical analysis agree well with the experimental results. The nonlinear hysteresis behavior of bridge column specimen was simulated under 2008 Wenchuan earthquake excitation and its failure modes were identical with the real earthquake damage of bridge column. The improved analytical models in the paper were applied to accurately predicting the nonlinear hysteresis behavior of RC bridge columns with strength and stiffness degradations and the pinching effect subjected to strong earthquake motion. Unscented Kalman filter （UKF） was applied to estimate the parameters of Bouc-Wen model in this paper, the simulation results and error analysis show that the method can accurately estimate the parameters of improved Bouc Wen model.