摘要
强动力荷载作用下结构大多发生非线性行为,不同结构的恢复力模型差别很大难以用统一模型描述,且在实际工程中难以测量所有自由度的动力响应。近年来粒子群优化算法被逐渐应用于结构系统识别领域,但大多局限于线性系统。因此,提出一种基于粒子群算法的非线性结构识别方法,该方法能够在结构部分自由度动力响应未知的情况下,不依赖于特定的参数化模型对结构质量以及非线性恢复力进行识别。同时对粒子群算法的惯性权重进行改进,以减小计算误差。在线性多自由度系统中引入两种不同磁流变阻尼器,在模拟非线性行为的同时,验证方法的一般性和识别多处非线性行为的能力。通过数值模拟结果比较,证实了所提方法以及惯性权重改进的有效性。
Nonlinear behaviors often occur in structures under the action of strong dynamic loads.It is difficult to describe the restoring force models of different structures with a unified model,and it is also difficult to measure the dynamic response of all degrees of freedom in practical engineering.In recent years,particle swarm optimization(PSO)algorithms have gradually been applying in the field of structural system identification,but they are mostly limited to linear systems.Therefore,in this paper,a nonlinear structural identification method was proposed based on particle swarm optimization algorithm,which can identify the structural mass and nonlinear restoring force without relying on specific parameterized models in the case of unknown dynamic response of freedom of structural parts.At the same time,the inertial weight of the particle swarm optimization algorithm was improved to reduce calculation errors.Introducing two different magnetorheological dampers(MR)into a linear multi degree of freedom system,the generality of the method and its ability to identify multiple nonlinear behaviors was verified.Through the comparison of numerical simulation results,the effectiveness of the proposed method and the improvement of inertial weight was confirmed.
作者
陈天威
许斌
赵冶
CHEN Tianwei;XU Bin;ZHAO Ye(College of Civil Engineering,Huaqiao University,Xiamen 361021,Fujian,China;Key Laboratory of Intelligent Infrastructure and Monitoring of Fujian Province,Huaqiao University,Xiamen 361021,Fujian,China)
出处
《噪声与振动控制》
北大核心
2025年第5期59-64,共6页
Noise and Vibration Control
基金
国家自然科学基金资助项目(52378301)。
