基于粒子群算法的结构损伤诊断研究

发布时间：2019-01-27 11:44

【摘要】：土木工程结构在长期服役期间会受到环境荷载、疲劳荷载、化学腐蚀、材料老化等各种不利因素的影响,使其结构力学性能发生改变,从而导致结构完整性的破坏。通过设置结构健康监测系统,可以及时有效地诊断出结构损伤状态,避免安全事故的发生,因此,结构健康监测已成为当今世界广泛关注的热点研究话题。而利用何种结构损伤诊断技术对在役重大工程结构和基础设施的安全性进行评定和预警是当需要解决的问题。本文在已有研究成果的基础上,做了以下工作:(1)基于国内外对于结构损伤诊断技术研究的基础上,将传统的粒子群算法与频响函数相结合,利用实测频响函数和计算频响函数的形状相关系数CSAC及幅值相关系数CAF,构建了粒子群算法的目标函数,通过国际共享平台提出的benchmark结构对该优化算法进行了有效性验证,计算结果表明,本文所提优化算法与已有文献中的遗传算法相比,同样有效,有些情况下甚至优于遗传算法。(2)针对传统粒子群算法在收敛运行后期,粒子易陷入局部最优解,导致无法重新搜索,全局搜索性能下降,出现“早熟”现象等问题,提出了具体的改进措施,即对每次迭代过程中微粒位置进行干预,增强粒子群算法的认知能力和社会能力,提高了粒子群算法的多样性及全局搜索性能,改善了传统粒子群算法的不足。(3)利用改进后的粒子群算法与频响函数相结合,通过对一个五节间的桁架结构进行有限元模态分析,提取结构的前5阶固有频率以及损伤识别所需要完备信息,并与传统粒子群算法的识别结果进行比较,结果显示,改进后的粒子群算法收缩更快,且识别结果要优于传统的粒子群算法。
[Abstract]:Civil engineering structure will be affected by environmental load, fatigue load, chemical corrosion, material aging and other adverse factors during the long service period, which makes the mechanical properties of the structure change, resulting in the destruction of structural integrity. By setting up a structural health monitoring system, the structural damage state can be diagnosed in time and effectively, and the occurrence of safety accidents can be avoided. Therefore, structural health monitoring has become a hot research topic in the world. What kind of structural damage diagnosis technology should be used to evaluate and warn the safety of major engineering structures and infrastructure in service is a problem that needs to be solved. In this paper, based on the existing research results, the following works have been done: (1) based on the research of structural damage diagnosis technology at home and abroad, the traditional particle swarm optimization (PSO) algorithm is combined with the frequency response function (FRF). The objective function of PSO is constructed by using the measured frequency response function (FRF) and the shape correlation coefficient (CSAC) and amplitude correlation coefficient (CAF,) of the FRF. The validity of the PSO is verified by the benchmark structure proposed by the international sharing platform. The results show that the proposed optimization algorithm is as effective as the genetic algorithm in previous literatures, and in some cases it is even better than the genetic algorithm. (2) aiming at the late stage of the convergence of the traditional particle swarm optimization algorithm, The particle is easily trapped in the local optimal solution, which can not be searched again, the global search performance drops, the phenomenon of "precocity" appears, and so on. The concrete improvement measures are put forward, that is, the intervention of the particle position in each iteration process. It enhances the cognitive and social abilities of PSO, improves the diversity and global search performance of PSO, and improves the deficiency of traditional PSO. (3) the improved PSO is combined with FRF. Through the finite element modal analysis of a truss structure with five nodes, the first five natural frequencies of the structure and the complete information needed for damage identification are extracted. The results are compared with those of the traditional particle swarm optimization algorithm. The improved PSO algorithm shrinks faster and the recognition result is better than the traditional PSO algorithm.
【学位授予单位】：广西科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU317

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