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改进粒子群算法的两阶段梁式结构损伤识别
牛维枫1, 曹晖1,2
1.重庆大学 土木工程学院, 重庆 400045;2.重庆大学 山地城镇建设与技术教育部重点实验室, 重庆 400045
摘要:
针对实际工程监测时损伤识别误差大的问题,提出一种基于改进粒子群算法的两阶段识别方案。第1阶段利用D-S证据理论融合算法进行损伤定位;第2阶段利用改进的粒子群算法,对定位结果进行修正,同时准确定量损伤。仿真算例和实验分析结果表明:由于第1阶段损伤定位减少了可能损伤单元的数量,第2阶段基于改进粒子群算法的搜索范围减小,能更准确地识别多损伤和小损伤的位置和程度,且抗噪性能良好。
关键词:  损伤识别  噪声  信息融合  粒子群算法
DOI:10.11835/j.issn.1674-4764.2018.06.017
分类号:TB123
基金项目:
Two-stage beam structure damage detection based on improved particle swarm optimization
Niu Weifeng1, Cao Hui1,2
1.School of Civil Engineering, Chongqing University, Chongqing 400045, P. R. China;2.Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, P. R. China
Abstract:
Considering large errors of damage identification during actual monitoring process, a two-stage detection method based on the improved particle swarm optimization(PSO)algorithm was proposed. The algorithm is then used to detect the structural damage locations preliminarily,and to validate the locating results and determine the damage extents precisely. The comparisons between the simulation and experiments show that the first phase of damage identification reduced the number of suspected damage locations, making the search domain of the second stage based on the improved particle swarm optimization algorithm significantly reduced. The presented two-stage recognition method can identify the damage locations and detect the damage extent precisely for multi-damage and small damage,with considerable capacity in anti-noise.
Key words:  damage identification  noise  information fusion  particle swarm optimization
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