摘要
涡激振动已经成为威胁桥梁结构安全、行人舒适度和结构耐久性的重要风险因素之一。节段模型风洞试验是检验大跨度桥梁涡振的重要方法,但提取和分析涡振数据一般依赖于经验,缺乏准确高效的手段。针对闭口钢箱梁悬索桥节段模型风洞试验数据,引入了含噪声的基于密度的空间聚类分析方法,在提取位移标准差和单频程度这两个特征值的基础上,将竖弯和扭转涡振工况从三个攻角的全部试验工况中识别出来。通过风速-振幅关系和涡振工况的时程与频谱特征,进一步验证了涡振工况提取和分析的准确性,证实了涡振具有大振幅和强单频的特点。
Vortex-induced vibration(VIV)has become a main risk of bridges concerning the structural safety,pedestrian comfort,and structural durability.Wind tunnel experiments of sectional models are important in the verification of VIV of long-span bridges.However,the recognition and analysis of VIV are usually empirical,inexact,and inefficient.Concerning the sectional-model wind tunnel tests of a suspension bridge with closed steel box girders,the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)method has been introduced in the analysis of the experimental data.The standard deviation and singlefrequency property of displacement time-series are selected as the characteristic parameters.Using DBSCAN,the bending VIV cases and torsional VIV cases are recognized from the wind tunnel cases under three attack angles.The variation of vibration amplitude with wind speed,as well as the temporal and spectral characteristics of the VIV cases,are further analyzed,by which the recognition and analysis of VIV have been proved to be precise.The VIV cases have large amplitudes and strong single-frequency property.
作者
端木玉
董浩天
DUANMU Yu;DONG Haotian(School of Naval Architecture and Ocean Engineering,Guangzhou Maritime University,Guangzhou 510725,China;Department of Civil Engineering,Shanghai University,Shanghai 200444,China)
出处
《结构工程师》
2023年第4期138-145,共8页
Structural Engineers
基金
广州市教育局高校科研项目——羊城学者项目(202032786)
广州市科技计划项目(202002030424)
广东省普通高校特色创新类项目(2019KTSCX123)
广东省普通高校认定类科研项目(2020KTSCX107)。
关键词
涡激振动
闭口钢箱梁悬索桥
风洞试验
机器学习
基于密度的聚类算法
vortex-induced vibration(VIV)
suspension bridge with closed steel box girders
wind tunnel test
machine learning
density-based clustering
