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基于混沌粒子群随机子空间的桥梁多点损伤识别

Multi-point damage identification of bridge based on chaotic particle swarm stochastic subspace
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摘要 受复杂荷载与不利环境影响,桥梁结构服役性能日趋退化。结构模态参数是结构整体力学性态的特征指标,通过敏感模态参数可对结构服役状态进行判识。针对传统模态识别存在虚假模态,易遗漏真实模态,且计算效率较低的不足,采用混沌局部搜索对粒子群算法进行改进,优化加速度信号加窗截断位置和大小,结合协方差随机子空间法对各子信号进行模态识别;构建基于比例柔度矩阵和均布荷载曲率的损伤识别模型,通过挠度曲率相对变化判断结构损伤位置和程度;并通过开展斜拉桥缩尺模型损伤识别试验,对本方法的有效性进行验证。研究结果表明:混沌粒子群随机子空间方法具有较好的模态识别精度,可实现斜拉桥拉索、主梁等典型损伤准确定量和定位识别;结构跨中位置发生损伤对结构性能影响更大,实际工程中应提高跨中区域主梁和拉索的承载力储备以提升结构安全性。 Due to the influence of complex loads and adverse environments,the service performance of bridge structures is gradually deteriorating.Structural modal parameters are the characteristic indexes of the overall mechanical behavior of the structure,and the structural service state can be identified by sensitive modal parameters.This paper mainly focused on the problem of false modes,easy omission of real modes,and low computational efficiency in traditional modal recognition.The chaotic local search was used to improve the particle swarm algorithm,and the position and size of the window truncation of the acceleration signal were optimized.The modal identification of each sub-signal was carried out by combining covariance-driven stochastic subspace identification.A damage identification model based on proportional flexibility matrix and uniform load curvature was established,and the location and degree of structural damage were determined by the relative change of deflection curvature.Finally,the effectiveness of the proposed method was verified by conducting damage identification tests on the scaled models of cable-stayed bridges.The results show that the proposed chaotic particle swarm stochastic subspace method had a better accuracy of modal identification,and could realize accurate quantitative and location identification of typical damages such as cables and girders of cable-stayed bridges.The damage at the mid-span position of the structure had a greater impact on structural behavior.In practical engineering,the bearing capacity reserve of the main beam and cable in the mid-span area should be increased to enhance structural safety.
作者 马亚飞 汤洋 李祚 王磊 MA Yafei;TANG Yang;LI Zuo;WANG Lei(School of Civil Engineering,Changsha University of Science&Technology,Changsha 410114,China)
出处 《交通科学与工程》 2023年第6期12-23,共12页 Journal of Transport Science and Engineering
基金 国家重点研发计划项目(2021YFB2600900) 长沙理工大学研究生实践创新与创业能力提升计划项目(CLSJCX23031)。
关键词 桥梁工程 损伤识别 模态参数 随机子空间 混沌粒子群寻优算法 bridge engineering damage identification modal parameter stochastic subspace chaotic particle swarm optimization
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