情态动词
桥(图论)
跨度(工程)
鉴定(生物学)
贝叶斯概率
悬挂(拓扑)
贝叶斯推理
光学(聚焦)
计算机科学
算法
工程类
数学
人工智能
结构工程
医学
生物
物理
材料科学
内科学
植物
光学
同伦
纯数学
高分子化学
作者
Jianxiao Mao,Xin Su,Hao Wang,Hongyuan Yan,Hai Zong
标识
DOI:10.1142/s0219455423501948
摘要
Closely spaced modes commonly observed in long-span suspension bridges can greatly increase the difficulty of identifying and tracking modal parameters. Most existing studies generally focus on identifying the closely spaced modes and quantifying the uncertainties based on numerical and experimental models. Further research focusing on full-scale long-span bridges is still required. A case study on identifying the closely spaced modes of the Qixiashan Yangtze River Bridge, a long-span suspension bridge with a main span of 1 418 m, is conducted in this paper. The effectiveness of the generalized fast Bayesian fast Fourier transform (GFBFFT) method is verified by both the simulated and monitoring data. The results show that a larger coefficient of variation (COV) and higher uncertainty is typically contained in the closely spaced modes than the separated modes. Compared with the FDD and SSI methods, the GFBFFT method guarantees higher identification accuracy of modal parameters and can serve as a reliable tool to identify the closely spaced modes.
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