循环遗传聚类法稳定图自动分析  

Automatic analysis of stabilization diagram using iterative genetic-fuzzy clustering method

作　　者：苏亮[1] 宋明亮[1] 董石麟[1] 罗尧治[1] 

机构地区：[1]浙江大学建筑工程学院,浙江杭州310058

出　　处：《浙江大学学报（工学版）》2017年第3期514-523,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家"十二五"科技支撑计划资助项目(2012BAJ07B03)

摘　　要：提出一种迭代的基于遗传算法和模糊聚类(IG-FC)技术的稳定图自动分析方法.该算法通过任意给定一个初始聚类数目,迭代使用遗传算法和模糊聚类技术搜索出代表真实模态的聚类中心,最终自动剔除稳定图中的虚假模态.一旦程序参数给定后,可在没有任何人为主观干扰的情况下自动识别出结构的真实模态.使用一个七自由度的弹簧质量数值模型以及加拿大某座钢筋混凝土框架结构大楼、瑞士Z24桥的加速度实测数据验证所提出算法的准确性.算例模态频率的识别结果表明:该算法可以自动且较准确地剔除稳定图上的虚假模态;可以自动判别并识别出相邻模态以及弱激励模态.An iterative genetic-fuzzy clustering（IG-FC）methodology was proposed to automatically interpret the stabilization diagram.With a randomly set initial clustering center number,the methodology attempted to search out the clustering centers that can represent the real modes by using genetic and fuzzy clustering algorithms iteratively.Finally,the spurious modes could be excluded automatically from the stabilization diagram.Once the parameters were set,the real modes of structure could thus be recognized without any human interaction.The proposed methodology was verified by a 7-degree of freedom（DOF）spring-mass model and the accelerometer data of a reinforced concrete frame structure in Canada and the Z24 bridge. The robust identification results of modal frequency prove that the proposed IG-FC methodology can automatically and accurately eliminate the spurious modes on the stabilization diagram.What＇s more,the methodology can automatically distinguish and recognize the adjacent modes and weak incentive modes.

关 键 词：自动识别 模糊聚类 遗传算法(GA) 稳定图 聚类数目 虚假模态 

分 类 号：TU311.3[建筑科学—结构工程]