摘要
发展基于Pareto多目标人工鱼群算法(Multi⁃Objective Artificial Fish Swarm Algorithm,MO⁃AFSA),解决结构健康监测中传感器位置多目标优化的问题。构建与观测模态线性独立性、结构损伤灵敏度和损伤信息冗余性有关的传感器位置多目标优化目标函数;改进人工鱼群算法的追尾和觅食行为,并引入外部档案集以处理寻优过程中的互不支配解,结合Pareto概念选取与理想点欧式距离最近的Pareto解为最优解;以三层平面钢框架结构为数值算例,用基于Pareto人工鱼群算法求解传感器位置多目标优化方案,并进行结构损伤识别。研究结果表明:用所提方法得到的传感器测点在结构中均匀分布,获取的结构损伤信息更为全面,冗余性低,振型独立性好,能够较精确地识别损伤位置和损伤程度,并且抗噪性能好。
An artificial fish swarm algorithm based on Pareto multi-objective optimization is proposed for optimal sensor placement in structural health monitoring.The modal independence,damage sensitivity and damage redundancy are firstly utilized to establish the sensor multi-objective optimization function.Then the rear-end and foraging behaviors in the artificial fish swarm algorithm are improved,and the external file sets are introduced for the centralized processing of the mutually non-dominating solutions in the op-timization process.The Pareto solution with the closest Euclidean distance at the ideal point is considered as the final optimal solu-tion.A planar frame structure is finally used as a numerical study to verify the proposed artificial fish swarm algorithm based on Pa-reto multi-objective optimization for sensor optimal placement.The results obtained from the proposed method give a fairly uniform spacing for the sensor locations,and the information obtained by the measurements is more comprehensive,with low redundancy and good mode independence.The damage detection results also indicate the robustness of the proposed method.
作者
张笑华
吴圣斌
方圣恩
陈凌秀
ZHANG Xiao-hua;WU Sheng-bin;FANG Sheng-en;CHEN Ling-xiu(College of Civil Engineering,Fuzhou University,Fuzhou 350108,China;China Railway Construction Real Estate Fuzhou CO.LTD.,Fuzhou 350102,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2022年第2期351-358,共8页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51608126)
福州大学科研启动项目(GXRC-20054)。
关键词
结构健康监测
传感器位置优化
人工鱼群算法
Pareto多目标优化
structural health monitoring
sensor optimal placement
artificial fish swarm algorithm
Pareto multi-objective optimi⁃zation
