摘要
为研究大跨度斜拉桥模态参数的不确定性,将遗传算法引入传统快速贝叶斯傅里叶变换法中,并采用高信噪比渐进估计值约束遗传算法的参数搜索空间,发展了一种大跨度桥梁的贝叶斯模态参数识别方法。利用悬臂梁数值模拟验证该方法的识别效率与精度;依托苏通大桥实测加速度数据应用上述方法开展大跨度斜拉桥的模态参数识别。在此基础上,探讨频带宽度系数对模态参数识别精度和不确定性的影响,并分析模态参数后验概率密度函数(PDF)的分布特征。结果表明,所提方法可有效识别大跨度斜拉桥的各阶模态参数;频率和振型的不确定性较低,而阻尼比的不确定性较高;将频带宽度系数限制在5~10有利于保证识别误差与不确定性的平衡;模态参数后验PDF符合高斯分布。
To investigate the uncertainty of modal parameters of the long-span cable-stayed bridge,the genetic algorithm is introduced into the traditional fast Bayesian FFT(FBFFT)approach,and the asymptotic approximation with the assumption of high signal-to-noise ratio is used to constrain the search space of parameters of genetic algorithm.Thus,a modal parameter identification approach for the long-span bridge is developed.The recognition efficiency and accuracy of the proposed hybrid method are verified by using the numerical simulation of a cantilever beam.The modal parameters of Sutong Bridge are identified using the proposed method and according to the measured acceleration data.On that basis,the influence of bandwidth factor on the identification accuracy and the uncertainty of modal parameters are investigated.The characteristic of the posterior probability density function(PDF)of modal parameters is analyzed.The results show that every modal parameter of the long-span cable-stayed bridge can be identified effectively using the proposed method.The uncertainty of the identified frequencies and mode shapes is much lower than that of identified damping ratios.By setting the bandwidth factor between 5 and 10,the balance of identification error and uncertainty can be reached.The posterior PDF of identified modal parameters is highly consistent with the Gaussian PDF.
作者
杨朝勇
茅建校
王浩
张一鸣
YANG Chao-yong;MAO Jian-xiao;WANG Hao;ZHANG Yi-ming(Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education,Southeast University,Nanjing 211189,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2022年第3期691-698,共8页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51978155,52108274)
国家万人计划青年拔尖人才(W03070080)
中央高校基本科研业务费专项资金资助项目(2242020k1G013)。
关键词
模态参数识别
大跨度斜拉桥
不确定性
贝叶斯方法
遗传算法
modal parameter identification
long-span cable-stayed bridge
uncertainty
Bayesian approach
genetic algorithm
