1.沈阳建筑大学土木工程学院,辽宁 沈阳 110068
2.中国石油华东设计有限公司,山东 青岛 266000
苏醒(1993—),女,博士研究生。主要从事可恢复功能结构方面的研究。E-mail: suxingxing@stu.sjzu.edu.cn
收稿:2023-07-10,
修回:2023-08-13,
纸质出版:2024-02-15
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苏醒,阎石,付江监等.一种自复位摇摆墙的改进Bouc‑Wen滞回模型[J].防灾减灾工程学报,2024,44(01):39-49.
SU Xing,YAN Shi,FU Jiangjian,et al.An Improved Bouc‑Wen Hysteresis Model for Self‑Centering Rocking Walls[J].Journal of Disaster Prevention and Mitigation Engineering,2024,44(01):39-49.
苏醒,阎石,付江监等.一种自复位摇摆墙的改进Bouc‑Wen滞回模型[J].防灾减灾工程学报,2024,44(01):39-49. DOI: 10.13409/j.cnki.jdpme.20230710002.
SU Xing,YAN Shi,FU Jiangjian,et al.An Improved Bouc‑Wen Hysteresis Model for Self‑Centering Rocking Walls[J].Journal of Disaster Prevention and Mitigation Engineering,2024,44(01):39-49. DOI: 10.13409/j.cnki.jdpme.20230710002.
Bouc‑Wen 模型是一种可表征结构及构件刚度、强度退化等的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。自复位摇摆墙(Self‑centering rocking wall, SCRW)结构由于其优越的抗震和自复位性能,滞回曲线呈“旗帜型”。为了更好地表征这种“旗帜型”滞回特性,在 Bouc‑Wen 模型的基础上,建立一种具有较高精度和较好实用性的改进 Bouc‑Wen 滞回模型。改进的Bouc‑Wen 模型参数是决定结构滞回性能力学特征的关键。由于该模型参数众多且选择范围不明确,为适应该类模型参数高效识别的需求,通过在MATLAB / Simulink环境中搭建程序框图,实现了对该理论模型控制参数的定性及定量分析,并运用遗传算法对10个SCRW试验结果进行参数识别,对识别结果进行统计分析,建立各参数与滞回曲线关键点的关系式,基于统计结果给出了各参数的建议取值范围;最后,通过SCRW拟静力试验对改进的滞回模型和参数取值范围进行了验证。结果表明:这种单自由度改进的 Bouc‑Wen 滞回模型能较好地反映SCRW在往复荷载作用下无残余变形和强度、刚度退化特点的“旗帜型”滞回特性。所提出的参数取值范围显著提升了改进Bouc‑Wen 模型的识别精度与效率,识别过程对类似模型的参数识别具有参考价值。
The Bouc-Wen model is a multifunctional nonlinear smooth hysteresis model that can characterize the stiffness and strength degradation of structures and is widely used to describe the hysteresis behavior of various types of structures. Self-centering rocking wall (SCRW) structures have a “flag-shaped” hysteresis curve due to their superior seismic and self-centering performance. Aiming at characterizing the “flag-shaped” hysteresis characteristics, an improved Bouc-Wen hysteresis model with higher accuracy and better practicality is developed based on the Bouc-Wen model. The parameters of the improved Bouc-Wen model are the key to determining the mechanical characteristics of the structural hysteresis performance. Due to the large number of parameters with undefined selection ranges of this model, the qualitative and quantitative analyses are realized to meet the demand of efficient identification of the parameters of the theoretical model, by constructing a flow chart in the environment of MATLAB / Simulink. Moreover, the genetic algorithm is applied to identify the parameters of 10 SCRW tests, and the identification results are statistically analyzed to establish the relationship between each parameter and the key point of the hysteresis curves. The suggested value ranges of each parameter are given based on the statistical results. Finally, the validation results of SCRW pseudo-static tests show that this single-degree-of-freedom improved Bouc-Wen hysteresis model can better reflect the “flag-shaped” hysteresis characteristics under repeated loading without residual deformation and strength-stiffness degradation. The proposed range of parameter values significantly improves the identification accuracy and efficiency of the improved Bouc-Wen model, and the identification process has a reference value for the identification of similar models.
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