基于智能算法的桥梁结构健康监测传感器优化配置研究
发布时间：2018-07-25 06:41
【摘要】：近年来,桥梁结构的建设得到快速发展,桥梁结构健康监测成为国内外研究的热点。合理的配置传感器是保证桥梁结构健康监测质量的前提,它对准确实时地获取桥梁结构健康状况信息,实现桥梁结构的监测和评估是至关重要的。有效独立法(]Effective Independent, EFI)等传统优化方法已经被应用于桥梁结构传感器优化配置中,但这些优化算法都有其各自的局限性。一些智能优化算法,如遗传算法(Genetic Algorithm, GA)等方法的发展以及它们在各个领域的广泛应用,为智能算法解决传感器优化配置问题提供了理论依据。 本文将免疫算法(]Immune Algorithm, IA)、人工鱼群算法(Artificial Fish School Algorithm, AFSA)、Memetic算法、混沌猴群算法(Chaotic Monkey Algorithm, CMA)引入桥梁结构健康监测传感器优化配置问题的研究中。利用智能优化算法对传感器的数量及位置进行优化,使得在传感器使用最少的情况下所采集的桥梁结构数据更加精确。最后,通过两个桥梁的算例,验证采用这些智能优化算法对不同的桥梁进行传感器优化配置的有效性,结果表明所用算法可以较好地实现桥梁结构传感器优化配置。 本文研究的内容包括如下： (1)以桥梁结构健康监测系统为基础,分析桥梁结构传感器优化配置问题,建立相关的数学模型。利用Ansys软件构造桥梁结构有限元模型(Finite Element Model, FEM),进行模态分析,提取模态振型,构建模态置信度(Modal Assurance Criterion, MAC)矩阵。 (2)针对桥梁结构健康监测传感器优化配置问题,将免疫算法、人工鱼群算法应用于拱桥的传感器优化配置中。为验证算法在桥梁结构传感器优化配置中的有效性,分别与遗传算法、粒子群算法进行了配置结果的比较,结果显示所用算法收敛速度快,寻优精度高。 (3)研究了Memetic算法、混沌猴群算法在桥梁结构传感器优化配置问题中的应用,以悬索桥为桥梁结构模型。在遗传算法中加入局部搜索策略构成Memetic算法,与遗传算法相比,基于Memetic算法的悬索桥传感器优化配置能够得到较好的结果。将混沌理论引入猴群算法中,提出了基于混沌猴群算法的悬索桥传感器优化配置方法,结果表明混沌猴群算法能够较好地解决桥梁传感器优化配置问题,较猴群算法有明显的优越性。
[Abstract]:In recent years, the construction of bridge structure has been developed rapidly, and the health monitoring of bridge structure has become a hot spot at home and abroad. The reasonable configuration of sensors is the prerequisite to ensure the quality of bridge structure health monitoring. It is very important to obtain the information of bridge structure health condition accurately and real-time and to realize the monitoring and evaluation of bridge structure. Traditional optimization methods such as effective Independent method (Effective Independent, EFI) have been applied to the optimal configuration of sensors in bridge structures, but these optimization algorithms have their own limitations. The development of some intelligent optimization algorithms, such as genetic algorithm (Genetic Algorithm, GA), and their wide application in various fields, provide a theoretical basis for the intelligent algorithm to solve the problem of sensor optimal configuration. In this paper, the immune algorithm (] Immune Algorithm, IA), artificial fish swarm algorithm (Artificial Fish School Algorithm, AFSA) and chaotic monkey swarm algorithm (Chaotic Monkey Algorithm, CMA) are introduced into the optimal configuration of bridge structure health monitoring sensors. The intelligent optimization algorithm is used to optimize the number and position of the sensor, which makes the bridge structure data more accurate when the sensor is used least. Finally, through two examples of bridges, the effectiveness of using these intelligent optimization algorithms to optimize the sensor configuration of different bridges is verified. The results show that the proposed algorithm can achieve the optimal configuration of sensors in bridge structure. The main contents of this paper are as follows: (1) based on the health monitoring system of bridge structure, the optimal configuration of sensors in bridge structure is analyzed and the relevant mathematical model is established. The finite element model (Finite Element Model, FEM),) of bridge structure is constructed by using Ansys software to carry out modal analysis, to extract modal mode shapes, and to construct modal confidence degree (Modal Assurance Criterion, MAC) matrix. (2) to solve the problem of optimal configuration of bridge structure health monitoring sensors. The immune algorithm and artificial fish swarm algorithm are applied to the sensor optimization of arch bridge. In order to verify the effectiveness of the proposed algorithm in the optimal configuration of sensors in bridge structures, the proposed algorithm is compared with genetic algorithm and particle swarm optimization algorithm respectively. The results show that the proposed algorithm converges rapidly. (3) the application of Memetic algorithm and chaotic monkey swarm algorithm in the optimal sensor configuration of bridge structure is studied. The suspension bridge is used as the bridge structure model. Compared with genetic algorithm, the optimal configuration of suspension bridge sensor based on Memetic algorithm can get better results. The chaos theory is introduced into the monkey group algorithm, and a suspension bridge sensor optimal collocation method based on the chaotic monkey group algorithm is proposed. The results show that the chaotic monkey swarm algorithm can solve the problem of the optimal configuration of the bridge sensor. Compared with monkey group algorithm, it has obvious superiority.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U446
本文编号：2142961
[Abstract]:In recent years, the construction of bridge structure has been developed rapidly, and the health monitoring of bridge structure has become a hot spot at home and abroad. The reasonable configuration of sensors is the prerequisite to ensure the quality of bridge structure health monitoring. It is very important to obtain the information of bridge structure health condition accurately and real-time and to realize the monitoring and evaluation of bridge structure. Traditional optimization methods such as effective Independent method (Effective Independent, EFI) have been applied to the optimal configuration of sensors in bridge structures, but these optimization algorithms have their own limitations. The development of some intelligent optimization algorithms, such as genetic algorithm (Genetic Algorithm, GA), and their wide application in various fields, provide a theoretical basis for the intelligent algorithm to solve the problem of sensor optimal configuration. In this paper, the immune algorithm (] Immune Algorithm, IA), artificial fish swarm algorithm (Artificial Fish School Algorithm, AFSA) and chaotic monkey swarm algorithm (Chaotic Monkey Algorithm, CMA) are introduced into the optimal configuration of bridge structure health monitoring sensors. The intelligent optimization algorithm is used to optimize the number and position of the sensor, which makes the bridge structure data more accurate when the sensor is used least. Finally, through two examples of bridges, the effectiveness of using these intelligent optimization algorithms to optimize the sensor configuration of different bridges is verified. The results show that the proposed algorithm can achieve the optimal configuration of sensors in bridge structure. The main contents of this paper are as follows: (1) based on the health monitoring system of bridge structure, the optimal configuration of sensors in bridge structure is analyzed and the relevant mathematical model is established. The finite element model (Finite Element Model, FEM),) of bridge structure is constructed by using Ansys software to carry out modal analysis, to extract modal mode shapes, and to construct modal confidence degree (Modal Assurance Criterion, MAC) matrix. (2) to solve the problem of optimal configuration of bridge structure health monitoring sensors. The immune algorithm and artificial fish swarm algorithm are applied to the sensor optimization of arch bridge. In order to verify the effectiveness of the proposed algorithm in the optimal configuration of sensors in bridge structures, the proposed algorithm is compared with genetic algorithm and particle swarm optimization algorithm respectively. The results show that the proposed algorithm converges rapidly. (3) the application of Memetic algorithm and chaotic monkey swarm algorithm in the optimal sensor configuration of bridge structure is studied. The suspension bridge is used as the bridge structure model. Compared with genetic algorithm, the optimal configuration of suspension bridge sensor based on Memetic algorithm can get better results. The chaos theory is introduced into the monkey group algorithm, and a suspension bridge sensor optimal collocation method based on the chaotic monkey group algorithm is proposed. The results show that the chaotic monkey swarm algorithm can solve the problem of the optimal configuration of the bridge sensor. Compared with monkey group algorithm, it has obvious superiority.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U446
【参考文献】
相关期刊论文 前10条
1 何华;张力文;周建庭;杨建喜;周磊;;基于EI及MAC算法的连续刚构桥传感器优化布置[J];重庆交通大学学报(自然科学版);2011年05期
2 宗周红;孙建林;徐立群;李嘉维;;大跨度连续刚构桥健康监测加速度传感器优化布置研究[J];地震工程与工程振动;2009年02期
3 余波;邱洪兴;王浩;郭彤;;苏通大桥结构健康监测系统设计[J];地震工程与工程振动;2009年04期
4 李爱群,缪长青,李兆霞,韩晓林,吴胜东,吉林,杨玉冬;润扬长江大桥结构健康监测系统研究[J];东南大学学报(自然科学版);2003年05期
5 刘宇;毕丹;李兆霞;;大跨斜拉桥基于遗传算法的传感器优化布置方法[J];东南大学学报(自然科学版);2009年04期
6 李戈,秦权,董聪;用遗传算法选择悬索桥监测系统中传感器的最优布点[J];工程力学;2000年01期
7 黄维平,刘娟,李华军;基于遗传算法的传感器优化配置[J];工程力学;2005年01期
8 张连振;黄侨;王潮海;;基于多目标遗传算法的传感器优化布点研究[J];工程力学;2007年04期
9 陈树礼;苏木标;孙宝臣;冯小利;;光纤应变传感器在芜湖长江大桥长期健康监测中的应用[J];光通信技术;2005年11期
10 高维成;徐敏建;刘伟;;基于遗传算法的传感器优化布置[J];哈尔滨工业大学学报;2008年01期
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