基于改进布谷鸟搜索的Benchmark框架损伤识别

摘要
图/表
参考文献(14)
相关文章 (15)

全文: PDF (2866 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要工程优化问题中，布谷鸟搜索存在收敛精度不高、收敛速度较慢等弊端，本文从发现概率和随机步长两方面对布谷鸟搜索进行改进，并成功进行了实验室结构的损伤识别。首先，对基本布谷鸟搜索进行了改进，发现概率自适应调整，步长自适应变化。然后，基于MATLAB建立了英属哥伦比亚大学实验室ASCE Benchmark框架的3维有限元模型，并提取了6种损伤工况的实测频率和振型。最后，基于频率因子和振型因子建立了目标函数，分别采用基本布谷鸟搜索和改进布谷鸟搜索进行了损伤识别，结果表明，改进的布谷鸟搜索能够更好地识别结构的损伤位置和损伤程度。本文研究成果具有一定理论研究意义和较高的工程应用价值，可应用于实际工程的损伤识别和健康监测。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	黄民水
	乾超越
	程绍熙
	卢海林

关键词 ：损伤识别, 改进布谷鸟搜索, 自适应发现概率, 自适应步长, Benchmark框架模型

Abstract：There are many disadvantages when cuckoo search (CS) is adopted in engineering optimization, mainly the low convergence accuracy and slow convergence.So, the CS was improved in the aspects of discovery probability and steplength, which was then used in damage identification.In the improved cuckoo search (ICS) the adaptive discovery probability and adaptive step length were adopted.A 3D finite element model of the ASCE Benchmark frame model in the University of British Columbia was setup with the help of the Matlab and the experimental frequencies and vibration modes in six damage cases were extracted from the laboratory data.In the end, a damage identification objective function was established making use of the frequency factor and mode shape factor, and the damage identification was carried out based on CS and ICS.It is shown that the damage location and damage degree of the structure can be identified with better accuracy based on ICS.The results are of great theoretical significance and engineering value, and it can be applied in the damage identification and health monitoring in practical engineering.

Key words： Damage identification Improved cuckoo search (ICS) Adaptive discovery probability Adaptive step size Benchmark frame model

收稿日期: 2017-12-18 出版日期: 2018-11-15

引用本文:

黄民水, 乾超越, 程绍熙, 卢海林. 基于改进布谷鸟搜索的Benchmark框架损伤识别[J]. 振动与冲击, 2018, 37(22): 158-163.
HUANG Minshui,QIAN Chaoyue,CHENG Shaoxi,LU Hailin. Damage identification of the benchmark frame based on an improved cuckoo search. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(22): 158-163.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I22/158

[1] 王延伟, 宋旭坤, 温瑞智,等. 智能优化算法在结构损伤识别中的应用[J]. 世界地震工程,2011,27(4):44-51.
Wang Yanwei, Song Xukun, Wen Ruizhi, et al. Application of intelligent optimization algorithm to damage detection of structure[J]. World Earthquake Engineering, 2011,27(4): 44-51.
[2] 刘仁云, 于繁华, 张晓丽, 等. 基于多目标优化策略的结构损伤识别智能算法[J].吉林大学学报(理学版),2016, 54(2):303-308.
Liu Renyun, Yu Fanhua, Zhang Xiaoli, et al. An intelligent algorithm for structural damage identification based on multi-objective optimization strategy[J]. Journal of Jilin University (Science Edition),2016,54(2):303-308.
[3]丁政豪, 刘济科, 吕中荣. 基于改进蜂群算法的结构损伤识别[J]. 应用力学学报, 2017, 34(1):169-173.
Ding Zhenghao, Liu Jike, Lv Zhongrong. Damage identification of coupled system based on modified ABC algorithm[J]. Chinese Journal of Applied Mechanics, 2017, 34(1):169-173.
[4] Yang X S, Deb S. Engineering optimisation by Cuckoo search[J]. International Journal Mathematical Modelling and Numerical Optimisation, 2010, 1(4): 330-343.
[5] Walton S, Hassan O, Morgan K, et al. Modified Cuckoo search: A new gradient free optimization algorithm[J]. Chaos, Solitions& Fractals, 2011, 44(9):710-718.
[6] 王利英. 基于安全因子集的架桥机主梁结构损伤识别研究[D]. 北京: 北京交通大学, 2014.
Wang Liying. Study on damage identification of girder in bridge erection machine based on security factor subset [D]. Beijing: Beijing Jiaotong University, 2014.
[7] 陈亮, 卢厚清. 求解连续函数优化的自适应布谷鸟搜索算法[J]. 解放军理工大学学报:自然科学版, 2015, 16(3): 299-304.
Chen Liang, Lu Houqing. Self-adaptive cuckoo search algorithm for continuous function optimization problems[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2015, 16(3):299-304.
[8] 李荣雨, 戴睿闻. 自适应步长布谷鸟搜索算法[J]. 计算机科学, 2017, 44(5):235-240.
Li Rongyu, Dai Ruiwen. Adaptive step-size Cuckoo search algorithm[J]. Computer science, 2017, 44(5):235-240.
[9] 郑洪清, 周永权. 一种自适应步长布谷鸟搜索算法[J]. 计算机工程与应用, 2013, 49(10):68-71.
ZhengHongqing, Zhou Yongquan. Self-adaptive step cuckoo search algorithm[J]. Computer Engineering and Applications, 2013, 49(10): 68-71.
[10] Sophia H, Garrett D J. Identification of stiffness reductions using natural frequencies[J].Journal of Engineering Mechanics, ASCE, 1995, 121(10): 1106-1113.
[11] Doebling S W. Measurement of structural flexibility for experiment with incomplete reciprocity [D].PHD Thesis, University of Colorado, Department of Aerospace Engineering Science, 1995.
[12] 黄民水, 朱宏平, 李鹏辉. 结构模型修正中不同残差及权重系数研究[J]. 华中科技大学学报(城市科学版), 2008, 25(4):142-146.
Huang Minshui, Zhu Hongping, Li Penghui. Study on different residual and weight coefficient in model updating of structure[J]. Journal of HUST．(Urban Science Edition), 2008, 25(4):142-146.
[13] Johnson E A, Lam H F, Katafygiotis L S, Beck J L. Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data[J]. Journal of Engineering Mechanics, 2004, 130(1):3-15.
[14] Brincker R, Zhang L, Andersen P. Modal identification from ambient responses using frequency domain decomposion[J]. Journal of Engineering Mechanics, 2002, 30(5):441~448.