Bouc-Wen模型参数识别的非线性自适应遗传算法和试验验证

Bouc-Wen模型是一种可表征结构及构件在往复荷载作用下的刚度退化、强度退化等的一种多功能非线性光滑滞回模型，可广泛应用于各类结构滞回行为的描述。Bouc-Wen模型参数是决定结构构件滞回模型力学特征的关键，由于该模型参数众多且物理意义不明确，往往只能从滞回数据得到近似解。为适应该类模型参数高效识别的需求，该研究提出了一种非线性自适应遗传算法，并通过4片不同配筋和加载条件的RC剪力墙的低周反复加载试验对Bouc-Wen模型参数识别的效果进行了验证。模型参数识别得到的滞回曲线和算法效率与标准遗传算法识别的结果以及实验数据进行了对比，结果表明：所提出的方法显著提升了Bouc-Wen模型的识别精度与效率。该文所提出的方法可用来进行结构滞回模型的识别并用所识别的模型进行结构的非线性行为模拟。

A NONLINEAR ADAPTIVE GENETIC ALGORITHM FOR PARAMETER IDENTIFICATION OF THE BOUC-WEN MODEL AND ITS EXPERIMENTAL VERIFICATION

The Bouc-Wen model is a versatile nonlinear smooth hysteretic model that can characterize the stiffness degradation and strength degradation of structures and components under cyclic loads. It can be widely applied to describe the hysteretic behavior of various structures. The parameters of the Bouc-Wen model are critical to determining the mechanical characteristics of the hysteretic behavior of structural components. However, because there are many parameters of unclear physical meaning, the parameters are generally determined by approaching a group approximate solution from the hysteretic data. This paper aims at efficient identification of the parameters of the Bouc-Wen model and proposes a nonlinear adaptive genetic algorithm (NAGA). The efficiency is verified through the parameter identification of four RC shear walls with different reinforcement tested under low-cycle cyclic loading by using this algorithm. The hysteretic curves and algorithm efficiency obtained from parameter identification by NAGA and by the Standard Genetic Algorithm (SGA) are compared with the experimental data. The results show that the proposed method significantly improves the identification accuracy and efficiency. The proposed method can be used in hysterical model identification and corresponding nonlinear behavior simulation of structures.