基于蚁狮优化算法与迹稀疏正则化的结构损伤识别

摘要
图/表
参考文献(16)
相关文章 (15)

全文: PDF (1140 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对基于群智能结构损伤识别既有方法的识别精度和抗噪鲁棒性不足问题，提出基于蚁狮优化算法与迹稀疏正则化的方法求解结构损伤识别问题。首先将结构损伤识别逆问题转化为数学中的约束优化问题，并根据模型修正原理利用结构模态参数定义优化问题的目标函数；其次在目标函数中引入迹稀疏约束；最后通过不同损伤工况下简支梁损伤识别数值模拟以及钢管简支梁实验验证方法的有效性。结果表明，基于蚁狮优化算法与迹稀疏正则化的结构损伤识别方法能够有效修正有限元模型，在不同噪声水平和损伤工况下不仅能准确定位损伤位置而且能精确识别损伤程度。本文方法为结构损伤的现场识别提供了可能性。

	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
	陈承滨1
	余岭1
	2
	潘楚东1
	陈泽鹏1

关键词 ：结构损伤识别, 蚁狮优化算法, 迹稀疏正则化, 约束优化问题, 模型修正

Abstract：Classical swarm intelligence (SI) based structural damage detection (SDD) methods have some common deficiencies, such as low identified accuracy and low robustness.In order to solve the above problems, a novel method based on the ant lion optimizer (ALO) algorithm and trace sparse regularization was proposed to solve the SDD problem.First of all, the SDD inverse problem was transformed into a constrained optimization problem in mathematics.According to the model updating principle, the objective function of the SDD optimization problem was defined by the structural modal parameters.Then, the trace sparse constraint was introduced into the objective function.Finally, the validity of the proposed method was verified by numerical simulations of a simply-supported beam in damage patterns and by measurement data of a steel-tube simply-supported beam.The SDD results show that the proposed SDD method can effectively update the finite element model (FEM).Under different noise levels and damage patterns, the proposed method can accurately locate damages and quantify damage severities.

Key words： Structural damage detection antlion optimization algorithm trace sparse regularization constrained optimization problem model updating

收稿日期: 2018-04-12 出版日期: 2019-08-15

引用本文:

陈承滨1，余岭1,2,潘楚东1,陈泽鹏1. 基于蚁狮优化算法与迹稀疏正则化的结构损伤识别[J]. 振动与冲击, 2019, 38(16): 71-76.
CHEN Chengbin1，YU Ling1,2，PAN Chudong1，CHEN Zepeng1. Structural damage detection based on an ant lion optimizer algorithm and trace sparse regularization. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(16): 71-76.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I16/71

[1] Fan W, Qiao P. Vibration-based damage identification methods: a review and comparative study [J]. Structural health monitoring, 2011, 10(1): 83-111.
[2] 李惠, 周文松, 欧进萍,等. 大型桥梁结构智能健康监测系统集成技术研究[J]. 土木工程学报, 2006, 39(2): 46-52.
LI Hui, ZHOU Wen-song, OU Jin-ping, et al. A study on system integration technique of intelligent monitoring systems for soundness of long-span bridges [J]. China civil engneering journal,2006, 39(2):46-52.
[3] CHEN Ze-peng, YU Ling. A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications [J]. Measurement, 2018, 122: 658-669.
[4] 余岭, 徐鹏. 基于CACO算法的结构多损伤识别[J]. 振动工程学报, 2010, 23(5): 523-529.
YU Ling, XU Peng. A CACO-based algorithm for structural multi-damage identification [J]. Journal of Vibration Engineering, 2010, 23(5): 523-529.
[5] 巫文君，董利强，任晖，姜绍飞. 基于改进 MPSCO 算法的框架结构损伤检测研究[J]. 地震工程与工程振动, 2017, 37(2): 108-116.
WU Wen-jun, DONG Li-qiang, Ren Hui, Jiang Shao-fei. Damage detection of frame structures based on improved multi-particle swarm coevolution optimization algorithm [J]. Earthquake Engineering and Engineering Dynamics, 2017, 37(2): 108-116.
[6] 李成, 余岭. 基于人工鱼群算法的结构模型修正与损伤检测[J]. 振动与冲击, 2014, 33(2): 112-116.
LI Cheng, YU Ling. Structural model updating and damage detection based on artificial fish swarm algorithm [J]. Journal of Vibration and Shock, 2014, 33(2): 112-116.
[7] 丁政豪, 吕中荣, 刘济科. 基于差分人工蜂群算法的梁结构裂纹识别[J]. 振动与冲击, 2016, 35(11): 74-78.
DING Zheng-hao, LU Zhong-rong, LIU Ji-ke. Identification of beam structures’ cracks based on difference artificial bee colony Algorithm [J]. Journal of Vibration and Shock, 2016, 35(11): 74-78.
[8] Mirjalili S. The ant lion optimizer [J]. Advances in Engineering Software, 2015, 83(C):80-98.
[9] Talatahari S. Optimum design of skeletal structures using ant lion optimizer [J]. Iran University of Science & Technology, 2016, 6(1): 13-25.
[10] 栗然, 张凡, 靳保源,等. 基于改进蚁狮算法的电力系统最优潮流计算[J]. 电力科学与工程, 2017, 33(9).
LI Ran, ZHANG Fun, JIN Bao-yuan,et al. Optimal Power Flow Using Modified Ant Lion Optimizer [J]. Journal of electric power science and Engineering, 2017, 33(9).
[11] Nair S S, Rana K P S, Kumar V, et al. Efficient modeling of linear discrete filters using ant lion optimizer [J]. Circuits, Systems, and Signal Processing, 2017, 36(4): 1535-1568.
[12] Grave E, Obozinski G R, Bach F R. Trace lasso: a trace norm regularization for correlated designs[C]//Advances in Neural Information Processing Systems. 2011: 2187-2195.
[13] Bakhtiari-Nejad, Rahai, Esfandiar. Structural damage detection and assessment using in measured eigenvectors and eigenvalues [C]//7th MOVIC Conference, Washington University, 2004.
[14] 严鹏. 基于信号理论的桥梁健康监测降噪处理和损伤识别研究[D]. 成都: 西南交通大学, 2012.
YAN Peng. Reaearch on noise reduction and damage idetification in bridge health monitoring based on signal theory[D]. Chengdu: Southwest Jiaotong University, 2012.
[15] LU Zhong-rong, LIN Xie-xing, CHEN Yan-mao, et al. Hybrid sensitivity matrix for damage identification in axially functionally graded beams [J]. Applied Mathematical Modelling, 2017, 41: 604–617.
[16] PAN Chu-dong, YU Ling, CHEN Ze-peng, et al. A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection [J]. Smart Structures and Systems, 2016, 17(6): 957-980.