|Table of Contents|

Optimal sensor placement for large-scale stadium based on improved genetic algorithm(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2024年06期
Page:
19-30
Research Field:
建筑结构
Publishing date:

Info

Title:
Optimal sensor placement for large-scale stadium based on improved genetic algorithm
Author(s):
XING Guohua1;  CHEN Sijin1;  MIAO Pengyong1; 2;  ZHANG Wen1;  WU Yanru1;  LIU Mingliang2
(1.School of Civil Engineering, Chang'an University, Xi'an 710061, Shaanxi, China; 2. Shaanxi Architecture Science Research Institute Co., Ltd, Xi'an 710082, Shaanxi, China)
Keywords:
large-scale stadium;  health monitoring;  optimal sensor placement;  improved genetic algorithm;  reticulated shell structure
PACS:
TU311.3
DOI:
10.19815/j.jace.2023.10002
Abstract:
In order to efficiently and accurately monitor the health of large-scale stadium structures, a sensor optimization layout method based on multi population non-dominated recombination genetic algorithm(MP-RGA)was proposed according to the structural characteristics of large-scale stadium. Introducing dual encoding to solve discrete optimization problems, improving the search ability of the algorithm through multiple population strategies, and solving the diversity loss problem in the later stage of genetic algorithm optimization through non-dominated recombination. Finally, adaptive crossover and mutation strategies were adopted to improve the convergence speed of the algorithm in the early stage and the optimization ability in the later stage. Taking the roof of Xi'an international football center as an example, the effectiveness of the algorithm in determining the sensor layout scheme under different sensor layout numbers was verified through numerical examples. The results show that compared with quantum genetic algorithm(QGA)and particle swarm optimization algorithm(PSO), multi population non-dominated recombination genetic algorithm has a faster convergence speed, and the optimization effect with modal confidence criterion as the objective can be improved by 41.88% and 91.27%, respectively. The actual application effect of the sensor layout scheme optimized based on the proposed algorithm is good, indicating that the algorithm is suitable for the sensor optimization layout problem of large-scale stadium.

References:

[1] 杨 辰.结构健康监测的传感器优化布置研究进展与展望[J].振动与冲击,2020,39(17):82-93.
YANG Chen.Advances and prospects for optimal sensor placement of structural health monitoring[J].Journal of Vibration and Shock,2020,39(17):82-93.
[2]罗尧治,赵靖宇.空间结构健康监测研究现状与展望[J].建筑结构学报,2022,43(10):16-28.
LUO Yaozhi,ZHAO Jingyu.Research status and future prospects of space structure[J].Journal of Building Structures,2022,43(10):16-28.
[3]周宏元,张广才,王小娟,等.基于改进蝴蝶优化算法的结构损伤识别[J].振动.测试与诊断,2023,43(1):164-171,204.
ZHOU Hongyuan,ZHANG Guangcai,WANG Xiaojuan,et al.Structural damage identification based on improved butterfly optimization algorithm[J].Journal of Vibration,Measurement & Diagnosis,2023,43(1):164-171,204.
[4]KAMMER D C.Sensor placement for on-orbit modal identification and correlation of large space structures[J].Journal of Guidance,Control,and Dynamics,1991,14(2):251-259.
[5]杨雅勋,郝宪武,孙 磊.基于能量系数-有效独立法的桥梁结构传感器优化布置[J].振动与冲击,2010,29(11):119-123,134.
YANG Yaxun,HAO Xianwu,SUN Lei.Optimal placement of sensors for a bridge structure based on energy coefficient-effective independence method[J].Journal of Vibration and Shock,2010,29(11):119-123,134.
[6]CARNE T G,DOHRMANN C R.A modal test design strategy for model correlation[R].Albuquerque:Sandia National Laboratories,1994.
[7]YUEN K V,KUOK S C.Efficient Bayesian sensor placement algorithm for structural identification:a general approach for multi-type sensory systems[J].Earthquake Engineering & Structural Dynamics,2015,44(5):757-774.
[8]NIEMINEN V,SOPANEN J.Optimal sensor placement of triaxial accelerometers for modal expansion[J].Mechanical Systems and Signal Processing,2023,184:109581.
[9]GOMES G F,PEREIRA J V P.Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm[J].Evolutionary Intelligence,2020,13(4):571-591.
[10]杨振伟,周广东,伊廷华,等.基于分级免疫萤火虫算法的桥梁振动传感器优化布置研究[J].工程力学,2019,36(3):63-70.
YANG Zhenwei,ZHOU Guangdong,YI Tinghua,et al.Optimal vibration sensor placement for bridges using gradation-immune firefly algorithm[J].Engineering Mechanics,2019,36(3):63-70.
[11]张笑华,吴圣斌,方圣恩,等.采用Pareto人工鱼群算法的结构健康监测传感器位置多目标优化[J].振动工程学报,2022,35(2):351-358.
ZHANG Xiaohua,WU Shengbin,FANG Shengen,et al.Multi-objective sensor optimal placement for structural health monitoring based on Pareto artificial fish swarm algorithm[J].Journal of Vibration Engineering,2022,35(2):351-358.
[12]伊廷华,李宏男,顾 明.基于模型缩聚的广州新电视塔传感器优化布置研究[J].工程力学,2012,29(3):55-61.
YI Tinghua,LI Hongnan,GU Ming.Research on optimal sensor placement of Guangzhou new TV tower based on model reduction[J].Engineering Mechanics,2012,29(3):55-61.
[13]OSTACHOWICZ W,SOMAN R,MALINOWSKI P.Optimization of sensor placement for structural health monitoring:a review[J].Structural Health Monitoring,2019,18(3):963-988.
[14]高 博,柏智会,宋宇博.基于自适应引力算法的桥梁监测传感器优化布置[J].振动与冲击,2021,40(6):86-92,189.
GAO Bo,BAI Zhihui,SONG Yubo.Optimal placement of sensors in bridge monitoring based on an adaptive gravity search algorithm[J].Journal of Vibration and Shock,2021,40(6):86-92,189.
[15]詹杰子,余 岭.传感器优化布置的有效独立-改进模态应变能方法[J].振动与冲击,2017,36(1):82-87.
ZHAN Jiezi,YU Ling.An effective independence-improved modal strain energy method for optimal sensor placement[J].Journal of Vibration and Shock,2017,36(1):82-87.
[16]吴子燕,何 银,简晓红.基于损伤敏感性分析的传感器优化配置研究[J].工程力学,2009,26(5):239-244.
WU Ziyan,HE Yin,JIAN Xiaohong.Optimal sensor placement based on sensitivity analysis of structural damage[J].Engineering Mechanics,2009,26(5):239-244.
[17]吴向余,贺旭东,屈冲霄,等.矩形舵面应变模态测试中的传感器优化布置[J].振动.测试与诊断,2022,42(5):913-917,1033-1034.
WU Xiangyu,HE Xudong,QU Chongxiao,et al.Optimal placement of sensors in strain modal test of rectangular rudder surface[J].Journal of Vibration,Measurement & Diagnosis,2022,42(5):913-917,1033-1034.
[18]QIN X R,ZHAN P M,YU C Q,et al.Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm[J].Advances in Structural Engineering,2021,24(2):252-265.
[19]周广东,操声浪,刘定坤.基于自适应动态惩罚遗传算法的桥梁监测 无线测点优化研究[J].建筑科学与工程学报,2018,35(5):86-92.
ZHOU Guangdong,CAO Shenglang,LIU Dingkun.Generalized genetic algorithm integrating self-adaptive dynamic penalty for optimal wireless sensor placement in bridge monitoring[J].Journal of Architecture and Civil Engineering,2018,35(5):86-92.
[20]王宗山,丁洪伟,王 杰,等.基于正交设计的折射反向学习樽海鞘群算法[J].哈尔滨工业大学学报,2022,54(11):122-136.
WANG Zongshan,DING Hongwei,WANG Jie,et al.Salp swarm algorithm based on orthogonal refracted opposition-based learning[J].Journal of Harbin Institute of Technology,2022,54(11):122-136.
[21]顾清华,姜秉佼,常朝朝,等.求解大规模优化问题的改进麻雀搜索算法[J].控制与决策,2023,38(7):1960-1968.
GU Qinghua,JIANG Bingjiao,CHANG Zhaozhao,et al.An improved sparrow search algorithm for solving large-scale optimization problems[J].Control and Decision,2023,38(7):1960-1968.
[22]伊廷华,张旭东,李宏男.基于改进猴群算法的传感器优化布置方法研究[J].计算力学学报,2013,30(2):218-223.
YI Tinghua,ZHANG Xudong,LI Hongnan.Research on optimal sensor placement based on improved monkey algorithm[J].Chinese Journal of Computational Mechanics,2013,30(2):218-223.
[23]孙小猛,冯 新,周 晶.网壳结构健康监测中的传感器优化布置[J].计算力学学报,2010,27(3):482-488.
SUN Xiaomeng,FENG Xin,ZHOU Jing.Optimum sensor placement for health monitoring of latticed shell structure[J].Chinese Journal of Computational Mechanics,2010,27(3):482-488.
[24]GONG C Q,ZHU H S,GANI A,et al.QGA-QGCNN:a model of quantum gate circuit neural network optimized by quantum genetic algorithm[J].The Journal of Supercomputing,2023,79(12):13421-13441.

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Last Update: 2024-12-10